Title: A review of marine and coastal ecosystem services data and tools to incorporate this in to decision-making

Authors: Justine Saundersa*, Nicola Beaumontb, Jon Atkinsc, Nick Hanleyd, Aisling Lannine, Dan Learf, Tiziana Luisettig, Ece Ozdemirogluh, Tavis Pottsi,

a Fugro EMU Limited, 1 Mill Court , The Sawmills, Durley, Southampton, SO32 2EJ, England (justine.saunders@fugroemu.com)

b Plymouth Marine Laboratory, Prospect Place, Plymouth, DEVON, PL1 3DH, England (nijb@pml.ac.uk)

c The Business School, University of Hull, Hull HU6 7RX, England (j.p.atkins@hull.ac.uk)

d School of Management, University of Stirling, Scotland (n.d.hanley@stir.ac.uk)

e Marine Management Organisation, Lancaster House, Hampshire Court, Newcastle upon Tyne, NE4 7YH, England (aisling.lannin@marinemanagement.org.uk)

f Marine Biological Association, The Laboratory, Citadel Hill, Plymouth, Devon PL1 2PB, England (dble@mba.ac.uk)

g School of Environmental Sciences, Centre for Social and Economic Research on the Global Environment, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, England (Tiziana.Luisetti@uea.ac.uk)

hEFTEC 73-75 Mortimer Street, London, W1W 7SQ, England (Ece@eftec.co.uk)

i The Scottish Association for Marine Science, Scottish Marine Institute, Oban, Argyll, PA37 1QA, Scotland (Tavis.Potts@sams.ac.uk)

*corresponding author, e-mail: justine.saunders@fugroemu.com

1. Introduction

One of the five guiding principles of the UK Government's 2005 Sustainable Development Strategy "Securing the Future" is to use sound science responsibly and this still underpins the activities of government departments and organisations. The principle promotes an evidence-based approach to decision-making that ensures policy development is informed by evidence at each stage, from identifying and monitoring environmental issues, to the consideration of all available policy options to address the issue, development of the most appropriate response and subsequent evaluation of policy effectiveness.

The range and scale of pressures on the marine environment are rapidly increasing with multiple pieces of legislation that aim to monitor and regulate any issues, and multiple management organisations at many different levels implementing the legislation. In this complex environment it is critical to ensure that we have the right data to enable decisions to be made and outcomes to be monitored and assessed based on the best available evidence. We need to invest in research for new data and tools to apply that data, and to develop the ability to discriminate between evidence which is reliable and useful, and that which is not. Once assessed, these data need to be made available to ensure transparency in the decision-making process, to encourage open and rigorous public debate and to avoid unnecessary duplication of research.

Historically, marine policy, management and data collection has focussed primarily on environmental concerns. However, if policies are to be successful, both in terms of uptake and achieving their desired goals, it is increasingly accepted that in addition to environmental considerations they must also take into account social and economic factors. Coupling environmental data with social and economic data is critical to support decision-making to achieve sustainable development objectives (Defra 2011, United Nations 1987). Ecosystem services assessment aims to provide a more holistic view of the value that the marine environment provides human beings with and how this value may be impacted by policy decisions.

Ecosystem services research requires a large range of environmental, social and economic information to inform the various levels of the assessment framework (ref framework in Kerry’s chapter). Environmental information is needed on the distribution and status of marine features (e.g. habitats and species, sea space, sea water, substratum) along with an understanding of the ecological processes that influence them in order to appreciate the flow in marine ecosystems towards final services. In order to then assess human wellbeing derived from these final services (i.e. goods and benefits in ref framework in Kerry’s chapter) information is needed on how human beings value the marine environment and where and how this value is extracted.

As a consequence, in the commercial and academic arenas, the application of marine and coastal ecosystem services research to the understanding and management of the environment is a rapidly growing field (Liquete et al. 2013). However, such research is heavily dependent on the availability of data and tools to apply such data to decision-making. The Marine Environmental Data and Information Network (MEDIN) is an open partnership which promotes sharing of, and improved access to, marine data in the UK. The MEDIN datasets for natural science data are comprehensive and well developed, but until recently the capacity for social and economic data was minimal.

A project carried out by several of the chapter authors developed and analysed a metadata catalogue of UK marine social and economic data important for marine planning and decision-making (MMO and Marine Scotland, 2012a ). The project was funded by the Marine Management Organisation (MMO) and Marine Scotland and managed by MEDIN. The study explored datasets and projects that assessed the value of ecosystem services and impacts on wellbeing as well as financial values or economic activity associated with uses of the sea (including data on the economics of supply chains and income distribution). Relevant social datasets included those relating to the characteristics of coastal communities: employment, demographics, business base, health and wellbeing data including access to recreational and leisure facilities, wealth / deprivation indices, quality of life indicators. The project also reviewed the tools available for incorporating such data into the various stages of decision-making (MMO and Marine Scotland, 2012b).

This chapter focusses on the project findings relevant to ecosystem services assessment, such as the availability of marine social and economic data on ecosystem services. Secondly, building on Chapters x and y, issues regarding the interpretation of socio-economic data are discussed, specifically including how to handle uncertainty within data and qualitative data. The chapter then provides a summary review of the tools that may facilitate the incorporation of this data into decision-making. The study was based in the UK but many of the findings and recommendations for the future research agenda are globally transferable. Finally a series of recommendations are provided, addressing these recommendations will augment understanding of the relationship between people’s desire to use elements of the marine environment and its varying importance to society and ecology.

2. An overview and analysis of marine social and economic metadata

Overview by data type

Data relevant to marine ecosystem services and marine social and economic data more generally, were sourced and documented in a meta-database for further analysis.

With regard to ecosystem services, studies included the benefits of natural and man-made coastal defences, marine protected areas and water quality. However, there were very few ecosystem service specific studies, and few of these were original primary valuation studies, instead relying on existing socio-economic data already presented in the catalogue or unspecified primary research.

Data on ecosystem services may also be covered by economic datasets in the MEDIN catalogue (MMO and Marine Scotland, 2012a) under categories such as fisheries and education. However, it is important to note that these datasets do not mean that all management, policy and academic needs can be met. For example there are extensive and readily available fishery datasets but less consistent data to enable assessments on whether stocks are being fished sustainably.

There are few social datasets but these are generally very comprehensive freely available national datasets such as those held by Office for National Statistics (ONS) and Economic and Social Data Service (ESDS) with internationally recognised protocols and standards for data collection and management. However, ONS data may be restricted to a particular spatial scale, e.g. regional level, with a charge for the provision of more detailed economic data at smaller local scales. In addition, although the ONS and ESDS datasets are very comprehensive they do not hold a lot of information specifically related to the marine environment and a number of complex assumptions are required in order to apply the data to marine policy and planning. For example, national data on tourism will not distinguish between that directly related to marine activities and other activities and careful assumptions are often needed to utilise such data.

The review also collated datasets on the geographic location of social and economic activities and infrastructure, both marine and coastal. This locational and other supporting data (e.g. habitat and species data) are useful when interpreting and applying the social and economic data. For example, in the case of the locational datasets these can be combined with economic and social data to provide an understanding of the spatial distribution of values. Scotland’s marine atlas holds the most up to date information on location of activities in Scottish waters, the MMO have published an extensive list of locational datasets for England used in marine planning on their website while the Crown Estate also provide access to their related activity data on the Marine Data Exchange. The availability of social and economic data specific for Wales is less clear although some data is likely accessible through the Welsh Government’s online statistics and research portal. Marine social and economic data for Northern Ireland is not held in a central place and may only be accessible through each individual government department.

Spatial distribution of data

The MEDIN catalogue was primarily focussed on collecting national level datasets, with comparatively lower effort expended on searching for small scale local datasets. However, exercises such as Strategic Environmental Assessment (SEA), marine planning and Marine Protected Area (MPA) projects have provided a number of regional datasets. It’s worth nothing that more local datasets may also be available from site specific Environmental Impact Assessments (EIA), Local Authority projects (particularly with respect to tourism and recreation) and local conservation group projects such as those by non-governmental organisations such as valuation studies of reserves managed by the Royal Society for the Protection of Birds.

Furthermore, whilst data may be available, it is not always at the appropriate spatial scale. For example, the tourism, leisure and recreation section is hindered by spatial issues. Data are either 1) collated at a national level, making marine-specific and spatially-allocated assessments difficult or 2) collated at a local or site-specific level, often for a single activity, making it difficult to scale up for national level assessments.

Temporal distribution of data

Temporal information regarding the datasets was poorly recorded in the metadata and therefore difficult to assess without delving further in to each individual dataset. Of the social and economic datasets 60% of metadata provided temporal information regarding when the data began or ended. Given multiple changes in organisations over time it can be difficult to trace down historical data. A significant number of the social and economic datasets were collected as part of one-off projects which were funded to support marine management and influence future policy, for example Charting Progress 2 (CP2), Scotland’s Marine Atlas, the National Ecosystem Assessment (NEA) and MPA projects. Even those projects that are updated regularly may be dependent on and vulnerable to funding from internal memberships, for example the British Marine Federation economic reports on recreational boating activity. Social datasets were particularly disparate and often held in individual project reports, such as studies on the social implications of fishing traditions. Thus policy developments greatly influence the age range of data and its consistency and maintenance over time.

Application/utilisation of data

As noted above, a large number of datasets were produced for specific applications in decision-making. However, explicit evidence of the direct application of the data in policy development and marine management can be unclear and specific information on how the data have been used may only be available from the supporting documents, if at all.

This is a missed opportunity as there is much to be learned by sharing knowledge of data application and utilisation, and building on previous experiences rather than repeatedly coming to the same issues and each time having to “reinvent the wheel”. Integrating understanding of limitations and knowledge gaps as well as being clear about how data are used in marine management and the decision and policy making processes could result in more effective and efficient research, enable the ready interpretation of data and shared understanding between regulatory and academic sectors. An agreed and consistent set of assumptions or methods for applying economic data would help to ensure a consistent approach to decision-making, and it is only by increasing the transparency of data use that this will be achieved.

To contribute to this, work is currently underway to assign Digital Object Identifiers (DOI’s) to datasets, following a similar model to peer reviewed literature, to enable datasets to be “citeable” and thus improve the tracking of their use (BODC and MEDIN). The UK government has committed to increasing transparency of data generally through its open data strategy and the development and promotion of the Open Government Licence (OGL) for public sector information. Additionally there are marine specific data sharing processes required to support the Marine Strategy Framework Directive (MSFD) and Common Fisheries Policy (CFP) commitments. If supported throughout the community, these initiatives and others could greatly increase the availability of useful social and economic data for analysis and decision making.

3. Detailed analysis of data availability by ecosystem service category

This section provides an overview of data availability by ecosystem category focussing on the end goods and benefits derived from the marine environment (Figure 1). There are a number of data gaps related not just to social and economic data, but also the availability of environmental data on which to interpret and apply economic data (for example, the abundance and distribution of species used for fish feed and fertiliser).

There are no spatial layers describing the distribution of educational marine resources as data sources are too disparate and would require significant time and budgets to collate. The educational value of the marine environment and its spatial distribution therefore remain a large data gap. It may however be particularly important at a local-scale, e.g. places such as Plymouth where marine research forms a large part of the local economy. This information may be useful for marine planning purposes in understanding the wider economic value of marine plan areas.

It should be recognised that the methodologies for the valuation of some goods and benefits are still in development; therefore it is difficult to even assess what datasets might be required. For example, the economic value of the sea's ability to assimilate waste is complicated as it is partly dependent on demand (i.e. how much waste needs to be assimilated by the environment) and partly dependent on supply (i.e. the capacity of environmental processes and components to store, break down and regulate particular volumes, concentrations and types of waste). Datasets on the demand for waste management includes annual information on the location, volume, intensity, and monitoring of regulated discharge of wastewater and the disposal of hazardous and non-hazardous waste. Much of this information is managed by either the relevant environment agencies (EA, SEPA and NIEA) or Cefas (licensed disposal sites for dredged material). Datasets on the supply of natural waste management services is more disparate and relates to the distribution and quality of ecosystem components such as saltmarshes, benthic sediments and bacterio-plankton that store and breakdown contaminants and pollutants. Furthermore, no single data-layer exists on the location of naturally-occurring coastal defence features although environmental data exist on the location of saltmarshes and shallow subtidal sandbanks, for example, which might enable such a layer to be produced. The economic value of such features has been estimated (NEA, 2011 (Coastal Margins chapter 11)) and there are some more specific case studies (cross ref VNN chapter). There are also a number of guidance documents on assessing the costs of flood risk.

Fig. 1 Gaps in evidence on ecosystem goods and benefits

Service Group	Final Ecosystem Service	Goods/Benefits*	Status of information on value and distribution and issues
Provisioning Service	Fish, shellfish and other wild/farmed species etc.	Food: fish, shellfish, algae, Salicornia and other wild/farmed food	CP2, MCZ regional projects, MSFD assessments, ICES reporting, Cefas, Defra, Inshore Fisheries and Conservation Authorities (IFCAs), Seafish, Scotmap in Scotland and FishMap Môn in Wales.
Fish feed (wild/farmed/bait)	Fish species used for aquaculture feed include sandeel, herring, capelin and sprats but economic value poorly known as difficult to separate landings from that for human consumption.
Seaweed	Fertiliser and biofuels	The main species are large kelps from the genus Laminaria. Economic value poorly known and from disparate sources - see NEA (2011) for overview.
Ornamental materials (shells)	Ornaments	Economic value unknown
Aquaria materials (fish, seaweeds)	Aquaria	This refers to provision of materials for aquaria, rather than the educational value of aquaria which comes under cultural services. Economic value unknown
Genetic resources	Medicines and blue biotechnology	Examples of medicinal applications include use in potential cancer fighting drugs, commercial skin products, detoxification agents, anti-viral compounds, anti-allergy and anti-coagulant agents (Saunders et al., 2010b). Value unknown. See NEA (2011) for overview.
Regulating Service	Climate regulation (e.g. Carbon sequestration and storage)	Healthy Climate	Disparate sources of research on the scale of carbon sequestration and storage although unit values agreed.
Natural hazard protection – i.e. from sandbanks & other habitats	Sea defence	Economic value estimated: raw data exists, e.g. on the distribution of natural features that provide natural hazard protection to collate a spatial layer of values.
Prevention of coastal erosion
Clean water and sediments	Waste burial, removal and neutralisation	Quantities of waste stored and broken down has been estimated for some habitats or marine features but economic value poorly known for reasons given in text (see also review in NEA, 2011, Chapter 12)
Cultural Service	Places and seascapes	Opportunities for tourism and nature watching (including recreation)	Social and economic data includes participation and visitor numbers and the related economic turnover from the sector. There are 31 spatial layers that might be used to spatially allocate these values.
Spiritual & cultural wellbeing (sites of religious/cultural significance; World Heritage Sites; folklore; TV & radio programmes; films)	Economic value unknown, cross ref relevant VNN chapter?
Aesthetical/inspirational benefits (Paintings, sculptures, books)	Economic value unknown, cross ref relevant VNN chapter?
Human health (physical and mental wellbeing, opportunities for exercise, local meaningful space, wilderness, personal space)	Economic value unknown, cross ref relevant VNN chapter?
Education/ecological knowledge (resource for teaching, cognitive development)	Datasets highly disparate and incomplete (see below)

*Legend: Dark grey = minimal social and economic or value data; Grey = some data, but insufficient to support impending policy needs; Unshaded = reasonable, but not perfect, data coverage

4. Interpretation of social and economic data

The previous sections highlight the extensive diversity of the social and economic datasets. The correct interpretation of these data are essential if they are to be used both efficiently and effectively within both academia, management and the decision making process. Two aspects of interpretation are covered in depth in this section: uncertainty; and utilisation of qualitative data. Other aspects, including aggregation of values, non-separable values incommensurable values and benefits transfer are covered elsewhere in the book (cross ref).

Addressing uncertainty in social and economic data

Information on the protocols and standards used to collect and analyse social and economic data was poorly represented within the metadata catalogue. Just 31 of the social and economic records had information on protocols, standards and quality control procedures. In addition only four records had specifically recorded information on uncertainties, known gaps or issues with the data, for example, information on years when surveys were not carried out, or information that the current dataset is undergoing review and is soon to be updated. This minimal discussion of uncertainties is common, however such information is critical in the interpretation of the data. Standardised approaches to metadata and increased awareness of the importance of metadata should improve this situation, both in terms of considering and reporting such issues.

With respect to ecosystem services there are many sources of uncertainty: in the distribution and quality of ecosystem components, understanding of economic value and the links between changes in ecosystem function/extent and consequent changes in service flows/benefits. Analysis of ecosystem services (whether for ‘financial / market’ or ‘non-market’ impacts) involves three steps (Figure 2):

qualitative analysis that identifies the links between the changes in ecosystem function / extent and consequent changes in service flows / benefits

quantitative analysis that produces biophysical and other data about this linkage

economic analysis (market and/or non-market) that also takes account of the social and economic characteristics of the affected human population.

Fig. 2 Steps of social and economic analysis of ecosystem services

There are gaps and uncertainties in each of these three steps. For example in the first step, we may not know the link between the change in the ecosystem and the change in the associated services. Such gaps in the knowledge are particularly relevant for some marine ecosystem services, for example bioremediation of wastes, which tend not to be as well studied as some of the terrestrial ecosystems or services.

The knowledge gaps in step (i) naturally continue in step (ii) as an unknown impact cannot be quantified. Even for known impacts, data may not exist, or data may exist in one location for one change context but may need to be ‘transferred’ to other locations / contexts. This transference introduces uncertainty, the scale of which would depend on the similarities between the two locations / contexts (Cross ref chapter on benefits transfer).

Step (iii) could suffer from three types of uncertainty. First, the value data may not exist or may not be specific to the location or context of interest. Second, the value data may exist but may not be robust (e.g. survey data may come from very small samples and may be for very specific changes that limit the transferability of the results). Third, the value data may exist, may be robust but be incomplete. It is rarely possible, even when primary valuation research is commissioned, to have monetary value estimates for each type and scale of ecosystem service change. Therefore, the third step of the analysis tends to produce results expressed in monetary and non-monetary units. The methods with which non-monetary estimates are reached and the extent to which they are included in marine management, decision making and policy appraisal also contributes to the uncertainty surrounding the results. Integration of research about valuing the environment (HM Government, 2011a, 2013, Saunders et al, 2010a) into analysis and decision making should reduce this type of uncertainty but much further work is required on this topic especially on practical application.

A final type of uncertainty stems from the scope of analysis. In most social and economic analyses (in all three steps) to date, the focus is usually on one pressure that causes ecosystem changes. If the same resource or service is subject to multiple on-going pressures, or to combinations of threats (natural or human induced), then an analysis focusing the baseline assessment on just one pressure could miss the dangers associated with the overall impacts. For example, when determining the impacts of aggregates extraction on fisheries it may be necessary to consider not only the direct impacts of extraction on fish habitats, but the bigger picture of threats facing fish populations, including overfishing, climate change and the availability of alternative habitats. Not doing so will lead to uncertainty in the results. Similarly most analyses do not take note of ‘cumulative’ impacts of previous changes and the one being analysed.

In terms of the factors that lead to uncertainty in individual economic value estimates, the following can be listed: the size of the impact being valued; the permanence of the impact; the affected (human) population; the valuation function; the timescale over which valuation occurs; and

the discount rate. Uncertainty (both in terms of gaps in data and uncertainty around the existing data) is clearly inevitable. How much of an obstacle it is to efficient decision-making depends on (i) how easy it is to handle uncertainty in the decision making context; (ii) how certain the data need to be for the purposes of a given decision-making context, or how much uncertainty is acceptable.

Handling uncertainty in the decision making context

For transparent decision-making, it is crucial to make clear what the evidence tells us, and what the uncertainties are. For this, a meta-analysis of the data used (or available) should be presented to the decision makers and academic user, identifying where the gaps are and what these gaps mean as well as what the data mean. This transparency is critical to guiding the strength of any management decisions or policy recommendations made on the basis of such estimates.

The level of uncertainty around an estimate can be gauged by undertaking sensitivity analysis, which involves re-estimating the economic analysis (e.g. Cost Benefit Analysis) using of a range of values for the parameters that represents the uncertainty about the true value. This is done by varying one parameter at a time to assess the effect on the result, which may be most appropriate when there are only a small number of parameters of concern. It is important to test as many assumptions as possible to see how different options fare in each run. There will likely be options that perform badly (e.g. negative net present value) in most of the runs which would strengthen the economic analysis recommendation against that option. The opposite could also happen whereby the strength of an option will be proven by it performing well in a large number of runs of the analysis.

Once the level of uncertainty has been determined, there are several methods available for clarifying the potential implications of this uncertainty for a given decision making context. The aim of these methods is not to reduce the uncertainty but to assist the marine manager or policy maker in understanding what uncertainty means in a specific context, and how to minimise the potential negative implications of this uncertainty. This requires good communication between the manager or decision/policy maker and the data provider. In addition, a number of more technical methods are available, including ‘minimax’ which selects between different options, having run the analysis under several different assumptions, so that the regret of making a wrong choice (selecting the wrong option) is minimised. ‘Regret’ in this context is defined as the difference between the net present value of the chosen option and that of other options.

When there are multiple parameters, each with significant uncertainties, Monte Carlo Analysis is the most appropriate method as it allows for these uncertainties to be assessed simultaneously in a single procedure. Monte Carlo Analysis relies on repeated random sampling to compute uncertainty estimates. Over a given domain of possible values (the range of values and the probability distribution across this range), repeated iterations generate a best estimate for the valuation and a confidence interval within which this value is expected to lie. In certain distributions (e.g. normal or triangular) the probability assigned to extreme values is very small, but their inclusion is necessary to ensure a true representation of the degree of uncertainty surrounding the estimate. With increased iterations, more confidence can be held in the results produced by the analysis.

A strategic approach to use, especially when probabilities are not known, is the switching analysis. This helps answer the question ‘how wrong the analysis needs to be for the results (the selected option) to be different, in other words, for the positive net present value for an option to become negative?’ The switching value (in percentage) for benefits can be estimated simply by deducting the present value of benefits from the present value of costs and dividing the resulting amount by present value of benefits, vice versa for switching value for costs. The higher the percentage value the less the influence of an individual (an uncertain or absent) factor on the results is. Note that this analysis does not change the level of certainty in the results, but presents the ‘comfort zone’ around them.

How much uncertainty is acceptable?

In terms of how much uncertainty is acceptable, there is no theoretical benchmark against which a given uncertainty level can be judged. For risk information, when the probability and/or magnitude of change is known, the smaller the probability is the better. For uncertainty, this is left to how risk averse the decision context can afford to be and one way for decision makers to assess this are the costs of making a wrong decision (see above).

There is no method of reducing uncertainty to zero, and as a result it is important to ensure stakeholder engagement and ownership of a decision, together with all its uncertainties. This can only be achieved if stakeholders are involved in a decision from the start and work together to address uncertainty and its impacts (e.g. commission new research, agree on assumptions to be tested in sensitivity analysis etc.) rather than use uncertainty as a reason for disagreement.

If the level of uncertainty is considered unacceptable a valid option should be to delay the decision making until further data can be collected in order to minimise the uncertainty to acceptable levels. This occurred in Wales in 2013 where the recommendations for a network of Marine Conservation Zones (MCZs) has been delayed until more is understood about the potential ecosystem benefits and economic impacts. If the decision cannot be delayed, the reasons for progressing in an unsatisfactorily uncertain environment should be clearly communicated to all parties. In practical terms, economic valuation and cost-benefit analysis deal with risk (i.e. where probabilities are known) reasonably well, and with ambiguity (known outcomes, unknown probabilities) to some extent, through calculation of expected values and various forms of sensitivity analysis. However, economic methods are more limited where possible outcomes are unknown. In these cases, concepts of ‘safe minimum standards’ can be used for aspects of the natural environment that need to be safeguarded because they have critical functions that cannot be substituted or they are near critical limits. Unfortunately in most cases it is not possible to identify such limits, and in some cases a highly precautionary approach may not acceptable due to a disproportionate limitation on development.

Risk, uncertainty and how to deal with them in decision making and appraisal are covered in the Green Book (HM Treasury, 2011) which should be adhered to when thinking about these in the context of the marine environment. In addition, it is clearly essential that if data is to be used and applied successfully, accompanying information on uncertainties, risks, protocols, standards and quality control must also be reported.

Utilisation of qualitative data

Qualitative data play a unique role in environmental management, but are often misused, misinterpreted or taken out of the context from which they are developed. Qualitative data does exist and are accessible, but its use and meaning differ substantially from traditionally collected economic assessments. There is a substantial need for expanding and improving qualitative approaches and what they can contribute to the management and formation of policy in the marine and coastal environment.

How sectors, communities, or individuals respond to valuation, in the complex real life context of social, cultural and political action is at the heart of qualitative research. Detailed studies on valuation in a social context, based on qualitative methods, could be highly useful for informing managers, and policy and decision makers and actors of the potential consequences of implementation of an ecosystem service style policy instrument e.g. payments for ecosystem services or the development of green taxes or positive subsidies. How valuations are actually to be utilised in the real life social and political world is a question of interest to qualitative social researchers, and is one that can only be answered by deliberative qualitative means, based on contextual analysis of the relevant political, social, and economic system, its networks and its stakeholders. These questions are likely to be controversial and result in public debate, resulting in winners and losers, and shifts in the allocation of resources.

While qualitative research is limited in terms of informing a measurable value for a particular habitat or species (and often contests the utility of such approaches), its benefit lies in understanding how the social world would respond to a particular policy instrument or scenario, and directly informs and influences the deliberation of policy. Take for example a local wetland that is the focus of considerable recreational activity. While quantitative approaches may provide a figure indicating the recreational value of the site, this approach may not always include the deeper and significant social values that are attached to landscape (or seascape) but which are critical for political negotiation and implementation of policy.

An increasing number of studies have explored ‘intangible’ values of communities towards the sea (e.g. Potts et al, 2012). These data shows that societal perspectives that emphasise aesthetic as well as practical aspects of the seas and non-market ecosystem services (e.g. climate regulation and scenery) are rated as important as economic maritime activities. However, as expected, countries vary in their views on particular services with significant differences in interpretation of relative importance, highlighting that analyses should consider the national and cultural context. Such qualitative data illustrates that marine values captured through economic metrics are not necessarily those of most importance to individuals and there is a clear research challenge in including some of the less easily quantified aspects of the marine environment in planning and decision making.

The data on non-market values around cultural ecosystem services are a case in point in terms of a lack of clear methodological guidelines, definitions or applications. More work is needed on expressing these values in a qualitative manner alongside quantitative estimates, and recognising the utility of doing so.

Uncertainty surrounds the qualitative methodological processes used to determine values (methodological uncertainty) which are themselves subject to considerable debate. Furthermore such qualitative data underpinning cultural ecosystem services are inherently context dependent, rich, and non-transferable to the broader context. Caution should be used in extrapolation of qualitative datasets that cover deliberations over the meaning of place, power, and context, and while critical for understanding the application of the ecosystem services approach, contribute more to theoretical development rather than longitudinal assessment.

Including qualitative data in a metadatabase, such as the one explored here, is fraught with difficulty as qualitative data often do not fit into the set keywords and categories. However, this information is of key importance and further efforts must be made to describe datasets of a qualitative nature.

5. Tools

This section provides a general overview of the different types of tools, their functions and application throughout the planning process. The detailed review carried out for the MMO and Marine Scotland presented tools and products in relation to their general functions and assessed them for their various strengths and weaknesses and data requirements.

Tools to apply social and economic information into decision-making in the marine and coastal environment may have a number of different functions throughout the policy development and licensing process including:

Understanding the problem that needs management

Data mapping and visualisation

Development of policy or development options

Selection of sites to meet policy or development objectives

Assessment of the economic and social impacts of policy and development options

Monitoring and evaluation of policy objectives, targets and licensing conditions

A summary of the various processes that might be involved under each function is provided in Table 1. Examples of applications are given from a regulatory perspective under marine planning and a developer’s perspective under marine project development. Some of the tool functions are dependent each other, for example, development of policy options (and underlying policy objectives) requires an understanding of the issue that needs managing, and site selection tools require mapping and visualisation routines. Processes involved in the development of plan and project options and Impact Assessment should aim to identify objectives, key issues and useful indicators to assist in the development of monitoring programmes. It is also worth noting that some of these tools are required throughout the decision-making process, for example, tools for mapping and visualising data will be important throughout the planning process in terms of communication and stakeholder engagement.

The tool functions may involve both spatial and temporal models and to various degrees may help to:

Incorporate data from ecological, economic, and social systems

Transparently assess management alternatives and tradeoffs

Facilitate stakeholder participation and collaboration, community outreach and engagement

Evaluate progress towards management objectives

Table 2 provides a summary of the tools that are available under their various functions. Overall, there are a large number of tools available for mapping and visualising data ranging from published reports to webbased maps that allow users to manipulate existing data and stakeholders to add their own data. These tools are likely to be applicable at all stages of decisionmaking. Most of the online web tools are secured with longterm funding. This is important to ensure that the initial investment in developing the tool and the effort made by stakeholders to populate it with data is protected.

A number of tools exist to help policymakers identify likely policy and plan options from simple mapping exercises to complex and dataintensive simulation models. They are often useful in engaging with stakeholders and exploring the initial outcomes of different management options. There are few examples of such tools being used in the licensing process, although they could be used to develop alternatives to the proposed project for consideration in the Environmental Statement.

The tools explored in this review ranged from simple mapping exercises to complex environmental and economic models. The level of complexity required from a tool is generally dependent on the level of risk involved in the decision making process (encompassing social, economic and environmental factors), the spatial scale of a plan or project (i.e. ranging from the development of large zonal wind farms to small coastal marine works) and the temporal scales being considered (ranging from short term to long term projects).

Table 1: A summary of the processes involved for each tool function and relevant planning and licensing applications

Examples in Marine and Coastal management
Tool function	Processes involved	Marine Planning stages - Regulator led aspects	Marine Project stages - Developer led aspects
Understanding the problem	Identify issues, constraints and future conditions, baseline assessment (dynamic and spatial)	Before planning starts – assessing why a plan is needed	What is the need for the project? Is an EIA required?
Data mapping and visualisation	Gather metadata and data, QA data, identify confidence levels, map data, make data available	Stakeholder engagement, Ongoing plan communication	Stakeholder engagement, Ongoing project communication
Development of options	Define objectives, explore scenarios, develop alternative management measures or project options	Plan development	Project development including early feasibility studies
Site selection tools	Define site criteria, explore scenarios, develop alternative spatial configurations, resolve spatial constraints	Refine any spatial aspects of the plan	Refine any spatial aspects of the project
Impact assessment	Evaluate the costs and benefits, strengths and weaknesses of baseline and alternative options	Sustainability Appraisal of the plan	EIA of the project
Monitoring and assessment	Gather monitoring data, assess performance indicators and evaluate plan/project	Plan monitoring and review	Post-consent monitoring and review

Table 2 Summary of tools

Function	General methods	Specific Products
Data mapping and visualisation	Web maps Data catalogues Reports	The MMO Marine Planning Portal The Marine Conservation Zone portal National Marine Plan Interactive Marine Scotland Interactive MaRS COWRIE “routemap to data” project EVRI (The Environmental Valuation Reference Inventory)
Development of options	Virtual and real world simulations Mapping Modelling of outcomes Initial coarse-level impact assessment	CoastRanger Co$ting Nature Coastal Resilience Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Multiscale Integrated Models of Ecosystem Services (MIMES) ARtificial Intelligence for Ecosystem Services (ARIES)
Site selection	Geo-spatial modelling Cost optimisation models Mapping of constraints	MaRS Touch-table Marxan Multipurpose Marine Cadastre (MMC) MarineMap
Impact Assessment	Cost-Benefit Analysis (CBA) Cost-Effectiveness Analysis (CEA) Multi-Criteria Assessment (MCA) Trade-off Analysis Life-Cycle Analysis (LCA) Bioeconomic models Risk Assessment	MaRS Valuing Change in UK Seas DEFINITE (decisions on a finite set of alternatives) IMPLAN (IMpact analysis for PLANning) InVitro (www.cmar.csiro.au/research/mse/invitro.htm) SolVES (Social Values for Ecosystem Services) (http://solves.cr.usgs.gov ) EMDS (Ecosystem-based Management Decision Support) ( Cumulative Impacts model (www.nceas.ucsb.edu/globalmarine) SPICOSA ARIES, MIMES, InVEST
Monitoring and Assessment	Marine Integrated Decision Analysis System (MIDAS) Ecosystem Assessment and Reporting Tool (EAR)

Site selection tools specifically facilitate the development of spatial options to address policy issues. They have had limited application in planning initiatives in the UK as they can be difficult to engage with stakeholders and the assumptions used for assigning constraint criteria or cost optimisation models are not always easy to communicate. However, site optimisation models can be useful in engaging with stakeholders and facilitating the design of developments. Good practice guidance could be developed to assist the future application of such tools.

The largest functional area of tool proliferation is in assessing the impact of policies. There are at least seven general methods that are closely related to each other but vary slightly in the level of detail required, the involvement of stakeholders in the process and in the criteria being assessed. They can be broadly compared as follows:

CBA compares different policy options according to a full monetary analysis that may include values of ecosystem services;

CEA compares policy options according to an objective or outcome unit;

MCA compares policy options according to scores and weightings rather than monetary units;

LCA compares policy options according to alternative measures such as energy use or carbon emissions (monetary analysis may later be applied);

Risk assessment compares policy options according to levels of acceptable risk;

Bio-economic models compare policy options according to modelled interactions between the environment and human activities; and

Tradeoff analysis compares policy options according to stakeholder consensus often including one or several of the methods above.

Given the number of general approaches to impact assessment, it is not surprising that numerous products have been developed to facilitate decisionmakers. However, an email and telephone survey of University institutions specialising in marine impact assessment models indicated that there are few products developed specifically for application to the marine environment in the UK, and even fewer that incorporate ecosystem services. This is recommended as a key area for further development.

Despite the lack of specific products, there have been several applied projects to provide spatial interpretation of economic values from the marine environment in order to understand the distribution of value throughout the UK and to inform impact assessments of policy options. These projects include CP2 (UKMMAS, 2010), the UK National Ecosystem Assessment (NEA) (2011), Valuing Change in UK Seas (Saunders et al, 2010b) and the recent baseline developed to inform the Impact Assessment for the Marine Strategy Framework Directive (Defra and Marine Scotland, 2012). These projects have made a number of advances in understanding the assumptions and methodologies for spatially presenting economic values that have been agreed at a high level.

In the UK, the MMO and Marine Scotland have responsibilities towards ensuring the sustainable development of the marine environment. Very few of the tools explored fully investigated this issue. For example, there are generally good figures on turnover or GVA for economic activities such as commercial fisheries, but these figures do not adequately capture the flow of economic stocks and whether this return is sustainable. The ability to do this would require bio-economic modelling of each individual managed stock along with indicators of Maximum Sustainable Yield (MSY) and Sustainable Stock Biomass (SSB). Likewise, an assessment of the flow of non-renewable marine resources, such as oil and gas and marine aggregates, over time would require an understanding of stock estimates or estimates of the longevity of currently identified stocks and the increased costs involved in extracting increasing rarer resources. This information exists to varying levels of confidence, but agreed methods to use this raw data to prepare spatially allocated data layers of the “sustainable” economic value of an activity have not yet been explored.

The review has highlighted that limited tools are available for assessing effectiveness of policies and further work should be focused towards tools that help to monitor and assess the achievement of policies, plans or projects and their objectives. For example, tools being developed by the UK Marine Monitoring and Assessment Strategy (UKMMAS) community (led by JNCC) include models for assessing the distribution of anthropogenic pressures on the marine environment.

It is important that tools are able to account for uncertainty where the quality of input data may be poor so that decisions can be made with appropriate levels of confidence and to illustrate the need to apply a precautionary approach to policy development when confidence levels are low. Processes that facilitate this include confidence assessments that may additionally be mapped and sensitivity analyses of how small changes in underlying assumptions affect model outputs. A few of the impact assessment tools are known to have this added functionality including MaRS, DEFINITE and EMDS.

The tools explored encompassed a range of data themes, as listed in Section 1.1, including financial values associated with marine activities and their geographical location. Less common, particularly in the UK, was the inclusion in decisionmaking tools of indirect economic values (e.g. supply chain data and employment) and social data on the characteristics of coastal communities. This is partly due to the difficulties in spatialising data for the marine environment that have been collected according to terrestrial geographies.

As already noted above, the incorporation of ecosystem service data in decisionmaking tools is in an early stage of development and will require robust scientific data and methodologies in order to facilitate this.

6. Summary and Recommendations

6.1 Data

i. Data gaps: There is a clear management, policy and academic need for high quality UK-specific marine social and economic data (HM Government, 2011b, Liquete et al, 2013). However, as addressed in the previous sections it is apparent that there are significant limitations in the data available. It should also be noted that an extensive list of datasets does not mean that all the management, policy and academic requirements are met. These findings should help steer future research requirements.

ii. Use of data: Further clarity on how such data have been applied in management, policy and decision making could ensure that the data are further applied and utilised in an effective, efficient and accurate way and could improve the development of academic research, the application of social and economic data by industry and agencies, and increase the understanding of stakeholders.

iii. Spatial scope: The datasets identified by the MMO and Marine Scotland (2012a) study tend to be on a national level and this was symptomatic of the nature of the project objectives relating to metadata for marine planning which was not intended to cover local levels. Hence few location specific datasets can be found in the current catalogue. However, this information may be help by local councils, site-specific academic research, project-specific environmental impact assessment and monitoring and studies by local conservation groups. A case study at a local scale may be useful in highlighting the extent and diversity of this data.

iv. Links with natural science: Some of the data gaps are present due to a lack of natural science data and unless we can quantify the changes in the natural environment we cannot value those changes. Some social and economic data can be collected and applied independently of natural science data, but other techniques, particularly in the field of ecosystem services, are heavily dependent upon the natural science data. For example, initial Impact Assessments for MZCs (Defra et al, 2012) were unable to quantify beneficial ecosystem service changes without an understanding of the expected environmental change due to MCZ protection measures. The development of a data strategy for the marine environment should be undertaken with natural, social and economic requirements in mind.

v. Qualitative data: As previously mentioned both quantitative and qualitative data are required. Including qualitative data in management, policy and decision making will necessitate changes to some processes but could greatly improve the benefits for society. Qualitative information is of key importance and further efforts must be made to describe, categorise and publish datasets of a qualitative nature.

vi. Central repository: The lack of a central repository for metadata hinders its usage as datasets are spread across a disparate range of sources making it difficult for any potential user to find the relevant datasets. This could, and is, being addressed by the development of a central MEDIN portal and the encouragement of more open access agreements including the OGL. An agreement for management of this dynamic catalogue is required to ensure that the UK is implementing legislation based on a standard social and economic evidence base that can be used with confidence and constantly improved. Development of longer term and specifically marine-focussed, comprehensive datasets, such as further development of the ONS datasets would be valuable, but will require long term funding and a high level management strategy.

vii. Standardisation of approaches: To ensure the data are robust, transparent and defendable, best practice must be applied throughout the data life-cycle, from creation to long-term curation. Such best practice includes the creation of data to meet open, stable, internationally agreed standards and formats. Generating data in this way is critical to facilitate the greatest degree of interoperability and reuse, and to maximise the data’s value. To improve accessibility of data a single standard could be promoted. An understanding of the new importance of making social and economic data available to researchers, managers and policy makers could reiterate the necessity for making data accessible according to a standard. This would be in the interest of data providers and users as it could increase the efficiency and effectiveness of decision makers and overall management of the marine environment.

viii. Recording of metadata: It is found that poor recording of metadata (that is the information on the datasets) is limiting the use of social and economic datasets which are currently available. This was particularly noticeable with regard to temporal metadata, and also standards, protocols and quality control metadata. Information regarding the application of the data was also missing. Providing this information is clearly not currently in the culture of social and economic data collection. However this information is critically important if these data are going to be used or developed. The metadata and associated publishing protocols must be given an equal priority, to enable a high degree of visibility to the data, enabling wider utilisation of the data and also validation.

6.2 Recommendations on Tools

The following recommendations can be made following the outcomes of the review of tools to apply social and economic data to decisionmaking:

Explore the use of models to develop realistic alternative options for marine planning where this may in term help to inform the licensing process.

Develop good practice guidance on the application of site optimisation models in consultation exercises with feedback gathered from stakeholders on the suitability of different site selection tools.

Investigate the feasibility of adapting existing impact assessment models and guidance for specific use in marine policy development in the UK and incorporating assessments of ecosystem services and human wellbeing

Agree methodologies to provide spatial understanding of economic values through targeted workshops with government economists and industry groups building on the work from Charting Progress 2, the NEA and the MSFD

Support further research towards tools to help monitor and assess the achievement of policies, plans or projects and their objectives, such as the development of anthropogenic pressure benchmarks and associated spatial data layers

Support further research to incorporate information on ecosystem services in planning tools

Explore new methodologies and tools to better assess the sustainability of marine activities in the marine environment

7. Discussion

Implementation of more integrated and holistic management approaches such as the ecosystem approach, the Marine Strategy Framework Directive and marine planning highlight the requirement to include social and economic as well as environmental considerations into decision making. This has made social and economic data and its interpretation an area of increasing global relevance. This chapter builds on an MMO and MS (2012a, b) funded study to review the currently available social and economic data, potential issues associated with the application of this data, and tools available to apply the data in the context of management, policy making and academic research.

The MMO and Marine Scotland (2012a) metadata catalogue is a first attempt at collecting this information together, and is a valuable baseline to build on, but it is limited by the incomplete metadata. This catalogue is a snap shot of the current situation, and a number of initiatives are presently being undertaken to fill some of the limitations highlighted under government research frameworks and the follow on to the NEA project. There are also a wide variety of EU funded studies including VECTORS , ValMER , and ODEMM. Recommendations relating to ongoing social and economic data availability, management and publication are being progressed through the MEDIN partnership in collaboration with the key holders and users of these data types.

With regard to management there is a need for policy makers, regulators and their advisors to provide more detailed guidance regarding what data they require, and better integration between data collection initiatives and regulatory needs. The vesting of dedicated marine organisations, for example, the Marine Management Organisation and Marine Scotland, provides a central coordinating point which, in conjunction with other government departments, the Marine Science Coordination Committee, and academia could achieve strategic integration for data gathering and interpretation in the marine environment. The development of the metadata catalogue (MMO and Marine Scotland 2012a) provides a valuable step forward to achieving this. In addition, guidance is needed on how these data should be applied, highlighting potential difficulties and uncertainties, and providing advice on how to mitigate these issues. To enable the achievement of these goals there is a clear benefit in continuing to develop and maintain the social and economic metadata catalogue, which will help to ensure that these datasets are widely and easily available, ensuring their full application is realised and avoiding the collection of multiple overlapping and poorly integrated datasets.

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