A systematic evaluation of Scotland's Natural Capital Asset Index

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By publishing a Natural Capital Asset Index (NCAI) in 2011, Scotland became, “The ... 2012). The NCAI project developed out of SNH's Trends & Indicators work, ...
Scottish Natural Heritage Commissioned Report No. 751

A systematic evaluation of Scotland’s Natural Capital Asset Index

COMMISSIONED REPORT

Commissioned Report No. 751

A systematic evaluation of Scotland’s Natural Capital Asset Index

For further information on this report please contact: Paul Watkinson Scottish Natural Heritage Great Glen House INVERNESS IV3 8NW Telephone: 01463 725276 E-mail: [email protected] This report should be quoted as: Albon, S., Balana, B., Brooker, R. & Eastwood, A. 2014. A systematic evaluation of Scotland’s Natural Capital Asset Index. Scottish Natural Heritage Commissioned Report No. 751. This report, or any part of it, should not be reproduced without the permission of Scottish Natural Heritage. This permission will not be withheld unreasonably. The views expressed by the author(s) of this report should not be taken as the views and policies of Scottish Natural Heritage. © Scottish Natural Heritage 2014.

COMMISSIONED REPORT

Summary A systematic evaluation of Scotland’s Natural Capital Asset Index Commissioned Report No.: 751 Project no: 14872 Contractor: The James Hutton Institute Year of publication: 2014 Keywords Natural capital; ecosystem services; environmental quality indicators. Background In 2011 Scotland became, “The first country in the world to publish a detailed attempt to measure annual changes in its natural capital1, based on an evaluation of ecosystem service potential” (SNH 2012a). The Natural Capital Asset Index (NCAI) was developed as a measure of relative change in the extent and condition of each of seven ecosystems (Broad Habitats) weighted across ecosystem services2, and standardised to 100 in the year 2000. A single aggregate value for Scotland is derived by weighting across Broad Habitats. The motivation for developing the NCAI was two-fold. Firstly, as a measure which could easily communicate the overall change in Scotland's natural assets and highlight the factors which are driving change, and so inform action. Secondly, as a measure of the sustainability of Scotland’s economic development, this could potentially be used alongside GDP to reflect the nation’s overall wealth. The methodology adopted to construct the NCAI was informed by the early thinking about Experimental Ecosystem Accounting instigated by the UN Committee of Experts on Environmental-Economic Accounting (UNCEEA 2013). The NCAI is based on a range of pre-existing indicators/measures of ecosystems and the flow of services, available from government, government agencies, NGOs, etc. However, the robustness of the indicators and weighting system used to combine across ecosystem services, within and between ecosystems, has not been evaluated systematically before now. Main findings  Although the proportion of indicators assessed as ‘green’ (fit-for-purpose) was low (< 30% for all Broad Habitats), there were a large number viewed as ‘amber’ (possibly fit-forpurpose). Typically concerns in the ‘amber’ class were around the extent to which changes in the indicator reflected changes in the asset (cause-effect relationship). Where 1

Natural capital – the elements of nature that directly or indirectly produce value to people, including ecosystems, species, freshwater, land, minerals, the air and oceans, as well as natural processes and functions (NCC 2014). 2 Ecosystem services - the outcomes from ecosystems that directly lead to good(s) that are valued by people (UK NEA 2011a, b).

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indicators were scored ‘red’ (not fit-for-purpose) it was commonly because changes in the indicator were thought to be confounded by factors other than changes in the asset (common for cultural indicators), or the data was not sensitive to change, or when an indicator was extrapolated over more than five years. The consequence of excluding the ‘red’ indicators on the magnitude and trends in the NCAI was explored within all seven Broad Habitats. In general, it seemed to make little difference to the decadal trends, possibly reflecting the similarity of many of the indicators, which were at best ‘proxy’ measures of productivity of an asset (ecosystem service flows), rather than indicators of its functional capacity. However, there were sometimes substantial differences in the magnitude of fluctuations. To construct the aggregate national NCAI, the contribution of both Broad Habitats to services, and service groups within Broad Habitats were weighted, as was the contribution of each Broad Habitat to the national index. However, given limited quantitative data or only qualitative data in some cases, the relative importance of provisioning, regulating/maintenance services, and cultural services, was an ‘expert’ judgement. Consequently we explored how the NCAI for each of four Broad Habitats changed if the weightings of service groups were altered radically from those used in the original method, including a scenario where provisioning, regulating and cultural services were equal (33%), and another which contrasted with the original. In general, the decadal trends were concordant, irrespective of the weights. However, in some Broad Habitats the magnitude of relative change differed markedly, suggesting that decision-making about management intervention and policy development could be difficult, if the assumptions underlying the ‘expert’ weightings were viewed as contentious. The NCAI indicators have understandably focused on readily available measures. However, while many indicators measure ecosystem service flows, few are capable of detecting changes in the potential capacity (productivity) of natural capital assets to deliver ecosystem services. Thus the NCAI is a useful aggregate measure of ecosystem service flows rather than a reflection of changes in the condition of the asset (stock) and its capacity to sustain the flow of a suite of services. The risk is that the NCAI fails to detect deleterious change in natural capital stocks, and the threat of collapse in services. A way forward would be to focus on establishing the best measures of the functional capacity of natural capital assets to sustain the delivery of ecosystem services. Here there is an opportunity to review the Ecosystem Health Indicators recently proposed under the 2020 Challenge for Scotland’s Biodiversity. Of particular relevance are the plans for both the Freshwater Monitoring Plan and the Soil Monitoring Action Plan under the proposed CAMERAS Scottish Environmental Monitoring Programme. An advantage of linking the NCAI to the Scottish Environmental Monitoring Programme and, in particular, the Ecosystem Health Indicators, is that it should provide disaggregated data, at scales where local intervention could potentially restore degraded natural capital assets and enhance productivity. Furthermore, comparisons within Broad Habitats of paired measures of both ecosystem service flow and integrity of natural capital assets from multiple sites would enable the shape of the relationship between productivity and functional capacity of natural assets to be determined, including the existence of thresholds, as envisaged in the Natural Capital Asset Check (NCC Report 2014).

For further information on this project contact: Paul Watkinson, Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW. Tel: 01463 725276 or [email protected] For further information on the SNH Research & Technical Support Programme contact: Knowledge & Information Unit, Scottish Natural Heritage, Great Glen House, Inverness, IV3 8NW. Tel: 01463 725000 or research @snh.gov.uk

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Table of Contents

Page

1. 

INTRODUCTION



2. 

METHODOLOGY USED IN CALCULATING THE NCAI 2.1  Background and rationale 2.2  The hierarchical weighting of ecosystem services and Broad Habitats 2.2.1  Weighting of Broad Habitat role in delivery of ecosystem services 2.2.2  Weightings of importance of ecosystem services to Scotland 2.2.3  Weightings of the Broad Habitats 2.3  The weighting of condition indicators within ecosystem service groups and Broad Habitats

2  2  3  4  5  5  7 

3. 

THE EVALUATION OF THE CURRENT SUITE OF NCAI INDICATORS 3.1  Background 3.2  Development of a framework for evaluation of indicators 3.3  Results of the evaluation of the current suite of indicators

10  10  10  12 

4. 

THE EFFECTS OF EXCLUDING THE ‘RED’ INDICATORS ON THE NCAI 4.1  The ‘sensitivity’ of the NCAI to the inclusion of indicators

13  13 

5. 

THE INFLUENCE OF CHANGING THE WEIGHTINGS ON THE NCAI 5.1  Rationale 5.2  Approach 5.2.1  Ecosystem service group weights 5.2.2  Redistribution of provisioning weights on the relative importance of Broad Habitats 5.3  Apparent ‘sensitivity’ of NCAI to the weightings 5.3.1  Ecosystem service group weights 5.3.2  Redistribution of provisioning weights on the relative importance of Broad Habitats

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6. 

7. 

8. 

RECENT DEVELOPMENTS IN ASSESSING CHANGE IN NATURAL CAPITAL ASSETS 6.1  Background 6.2  Experimental Ecosystem Accounting - The SEEA 2012 6.3  Natural Capital Asset Check

15  16  16  16  18  18  18  19 

OPTIONS FOR THE REFINEMENT OF THE NCAI 7.1  Summary of the evaluation of indicators and weighting system used in the NCAI 7.1.1  Indicators 7.1.2  The weighting system 7.2  Future directions

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REFERENCES

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22  22  22  23 

ANNEX 1: THE ECONOMIC VALUE SOURCES USED TO CALCULATE THE RELATIVE CONTRIBUTION OF BROAD HABITATS TO THE DELIVERY OF PROVISIONING SERVICES

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ANNEX 2: THE RATIONALE FOR AGREEING THE ‘TRAFFIC LIGHTS’ FOR THE INDICATORS IN EACH BROAD HABITAT

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ANNEX 3: COMPARISON OF THE NCAI WITH AND WITHOUT THE ‘RED’ INDICATORS

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ANNEX 4: THE POTENTIAL FOR ADDITIONAL/ALTERNATIVE INDICATORS IN A REVISION OF THE NCAI

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Acknowledgements We are particularly grateful to all the help given to the project team by Ralph Blaney, who led the original work scoping and devising the NCAI. His advice was invaluable to understanding the way indicators were selected and the weighting systems developed. We also thank his successor Paul Watkinson for guidance in finalising this report. Our evaluation was steered by Roddy Fairley, Mary Christie, Sue Marrs, Claudia Rowse, Des Thompson and Paul Watkinson (all SNH), Rebecca Badger (SEPA), Joanna Drewitt and Daniel Hinze (both Scottish Government, RESAS) and Darren Mosely (FRS). We are grateful to all for their insights and helpful comments throughout the last year.

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1.

INTRODUCTION

By publishing a Natural Capital Asset Index (NCAI) in 2011, Scotland became, “The first country in the world to publish a detailed attempt to measure annual changes in its natural capital3, based on an evaluation of ecosystem service4 potential” (SNH 2012a). The motivation for developing the NCAI was primarily to help inform decisions on the degree to which economic development is being managed sustainably, in a way which could be easily communicated (Blaney & Fairley 2012). The aspirations were to raise awareness of the drivers of change in the nation’s natural capital assets and to facilitate enhanced methods for assessing natural capital (Fairley, pers. comm.). The methodology for constructing the NCAI is a development on an approach used by Netherlands Environment Agency (ten Brink 2007), which also influenced the early thinking instigated by the UN Committee of Experts on Environmental-Economic Accounting (UNCEEA 2013). However, the robustness of the indicators and weighting system has only ever been partially evaluated (Hambrey & Armstrong 2010). Since the publication of the NCAI there has been wider recognition of the importance of being able to detect change in the capacity of natural capital assets to sustain the delivery of ecosystem services, because ultimately this influences economic and other human activity (UNCEEA, 2013). For example, the first recommendation of the Natural Capital Committee is “the development of a framework within which to define and measure natural capital, which would draw on data and monitoring systems from across government departments, non-governmental and research organisations” (NCC 2013). The task of developing approaches to a natural capital asset check is a major part of the research being undertaken through the UK National Ecosystem Follow-on work, which will report in June 2014, and will be used by the NCC to inform their deliberations on metrics to measure changes in natural capital (NCC 2014). However, while the conceptual frameworks linking natural capital assets, ecosystem services and human well-being are becoming more unified, concerns remain about the appropriateness of some of the environmental, social and economic data used as indicators in the NCAI (Hambrey and Armstrong 2010). Therefore, SNH has commissioned this review of the NCAI to better understand its strengths and weaknesses. This evaluation is a preliminary step in seeking the wider adoption of the NCAI by others, including business and local authorities - an aspiration of the 2020 Challenge for Scotland's Biodiversity (Scottish Government, 2013). Here we report an evaluation of the NCAI in six further sections, beginning with section 2) Methodology used in calculating the NCAI, followed by a robust, 3) Evaluation of the current suite of NCAI indicators, in order to enable, 4) The effects of excluding the ‘red’ indicators on the NCAI. We continue by considering, 5) The influence of changing the weightings on the NCAI, before describing 6) Recent developments in assessing change in natural capital assets, and finally exploring 7) Options for the refinement of the NCAI.

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Natural capital – the elements of nature that directly or indirectly produce value to people, including ecosystems, species, freshwater, land, minerals, the air and oceans, as well as natural processes and functions (NCC 2014) 4  Ecosystem services are the outcomes from ecosystems that directly lead to good(s) that are valued by people (UK NEA 2011a, b). 

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2. 2.1

METHODOLOGY USED IN CALCULATING THE NCAI Background and rationale

The development of an index of Scotland's natural capital assets was undertaken to find a measure that, firstly, could easily communicate the overall change in Scotland's nature in a way that reflects the importance of our natural assets to Scotland’s people and prosperity. A natural capital asset index should highlight the factors which are driving this change, and so inform action. Secondly, SNH wanted to both explore and inform the wider goal of assessing natural capital as a measure of the sustainability of Scotland’s economic development, which could be used alongside GDP to reflect the overall wealth of Scotland (Blaney and Fairley 2012). The NCAI project developed out of SNH’s Trends & Indicators work, through a pilot research project, which included a workshop with SNH staff, Scottish Government, other SG Agencies and NGOs (Hambrey and Armstrong, 2010). It was hoped that the index would form a useful addition to the indicator information that SNH already publishes, and also, that it would be more widely adopted. The NCAI distinguishes ecosystems in Scotland using the ‘Broad Habitat’ classifications based on those from Countryside Survey 2007. This is similar to the approach taken by UK National Ecosystem Assessment (UK NEA 2011 a, b), though the marine ecosystem, beyond the coast, was not considered in the NCAI. Otherwise, the only differences are in some of the Broad Habitat ‘labels’ used (Table 1). For example, Cropland in the NCAI, which includes both arable land and improved grassland, is called enclosed farmland in the UK NEA. The NCAI is structured around a method devised by the Netherland’s Environment Agency (ten Brink 2007), where changes in extent (quantity) of a Broad Habitat are multiplied by changes in the condition (quality) of that Broad Habitat. The NCAI is a measure of relative change in the extent and condition of each Broad Habitat standardised to 100 in the year 2000. The intention is that is should reflect the Broad Habitat’s capacity to deliver ecosystem services. Table 1. The NCAI classification of ecosystems into Broad Habitats and their respective areas in Scotland (2000). Even over a decade there is comparatively little change in the extent of most Broad Habitats. However, Woodland has expanded a little at the expense of Moorland and Grassland, while Cropland appears to be quite variable from year to year, with some losses overall to urban settlement. Broad Habitat

Ecosystems included in Broad Habitat

Coast

Dunes, cliff, beach and tidal mud-flats

Cropland

Arable land and improved grazing

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Grassland Moorland Woodland Freshwater Greenspace

Rough/semi-natural grasslands Heather moor, montane and peatland/bog Woods/forests, including commercial forestry Lochs, rivers and fens Urban parks, gardens, etc.

18.0 39.3 19.6 6.5 1.0

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% Area 2.0

Figure 1. The grouping of a range of ecosystem services under the Common International Classification for Ecosystem Services (CICES) adopted in the NCAI methodology. Recognising that Broad Habitats vary in their capacity to deliver different groups of ecosystem services (see UK NEA 2011a, p.11), the method is based upon earlier versions in the evolution of the Common International Classification for Ecosystem Services (CICES 2013) framework (see Figure 1). This facilitates a hierarchical system of weighting provisioning, regulating/maintenance, and cultural services within each Broad Habitat. Also, the method weights across Broad Habitats (see Figures 2 & 3), to derive the overall NCAI index. Weighting the indicators of changes in ecosystem services within each Broad Habitat was done separately. 2.2

The hierarchical weighting of ecosystem services and Broad Habitats

Since there are major issues about the most appropriate methods of valuation across a diverse range of natural assets and the ecosystem services they produce, any weighting system has to be based around a combination of market values, non-market values, nonmonetary values, and expert judgement (Turner & Daily 2008). Here we go into considerable detail to explain the methods used because the existing descriptions are somewhat limited and not easily accessible. SNH adopted a hierarchical approach with three stages for weighting: the relative role of Broad Habitats in delivering each ecosystem service (2.2.1), the importance to Scotland of ecosystem services, between and within provisioning, regulating/maintenance, and cultural groups (2.2.2), and the area adjusted Broad Habitats weights (2.2.3), after multiplying weighting regimes 2.2.1 and 2.2.2.

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2.2.1

Weighting of Broad Habitat role in delivery of ecosystem services

The relative weights of Broad Habitats were obtained in different ways for each of the provisioning, regulating/maintenance, and cultural ecosystem service groups. For provisioning services the weights relate to published measures of the economic value of food, fibre, water, etc., attributed to each Broad Habitat (for details see Annex 1). The weights for the regulating and maintenance services were obtained from a survey of scientists (using SurveyMonkey), basically asking them to score the importance of each habitat in delivering each service. For cultural services different sources were used to estimate the importance of each of the three service sub-groups. The recreation value was based on SNH Recreation Survey (number of visits per Broad Habitat) multiplied by an enjoyment value associated with each Broad Habitat (from the Omnibus survey of the Scottish population).The heritage value was based just on the Omnibus survey, while the tourism value was from a survey of tourism experts. The Broad Habitat values shown in Figure 2 represent the percentage of a given service (the rows of the table each totalling to 100) attributed to each hectare of that particular Broad Habitat (columns of the table). The very high percentage of the provisioning service attributed to Freshwater (89.1%) reflects the high value of water (£520M; see Annex 1) and the fact that it accounts for a comparatively small area (6.5% or 171,654 ha in 2000) of the total area across all seven Broad Habitats. The estimate of provisioning services delivered by Freshwater habitats is therefore just over £3,000 per hectare, almost twenty times more than c. £160 per hectare for Cropland.

  Figure 2. The percentage of each ecosystem service attributed to each hectare of each Broad Habitat and the expert judgement of the importance of each ecosystem service to Scotland (Scotland Weight).

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2.2.2

Weightings of importance of ecosystem services to Scotland

Based on the survey of scientists, the Omnibus survey and the relative economic contribution of nature-based tourism, ecosystem service group weightings were derived, as 25% (provisioning), 50% (regulating/maintenance – split equally) and 25% (cultural). Specific services within each group were also weighted, for example, the Scotland-wide importance of carbon sequestration (weight 10) was estimated to be twice as important as the Freshwater quality regulation (weight 5) (see Figure 2, last column labelled ‘Scotland Weight’). 2.2.3

Weightings of the Broad Habitats

These weightings were generated by multiplying stages i) and ii) above. In other words, the Broad Habitat percentage values for the delivery of a given ecosystem service in Figure 2, multiplied by the national importance of that ecosystem service to Scotland (the ‘Scotland Weight’ in the last column in Figure 2) to produce Figure 3. For example, in the case of Woodland, multiplying the 2.4% contribution attributed to provisioning, by the ‘Scotland Weight’ of 25, gives 61 units (after rounding). Whereas, multiplying the 26% of all carbon sequestration attributed to woodland, by the ‘Scotland Weight’ of 10 gives 260 units. And so on.

Figure 3. The derived values of the delivery of each ecosystem service per hectare of each Broad Habitat, after weighting both the role of the Broad Habitat in delivering an ecosystem service and weighting the overall importance of each ecosystem service to Scotland (Figure 2). Comparing the aggregate measures (Grand Total) to the smallest value (Cropland) permits relative ranking of the Broad Habitats (red values in last row) and is used in the overall NCAI.

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Summing across all ecosystem service values within a Broad Habitat generated an aggregate measure for the contribution of that Broad Habitat, per hectare, in delivering a bundle of ecosystem services (penultimate row Figure 3). Comparing these aggregate measures between Broad Habitats suggests that Cropland delivers the smallest bundle of ecosystem services (649 units per hectare) and Freshwater the largest (3,278 units per hectare – five times as much as Cropland). These relative ranks (see the red values in the last row of Figure 3) were used to combine across Broad Habitats, by multiplying the area of a Broad Habitat by its relative rank, to give a Scotland NCAI. Figure 3 also provides the values to calculate the contribution of each ecosystem service group within a Broad Habitat (Figure 4). For example, in Woodland, the 61 units of provisioning services is 5% of the total 1,295 units of ecosystem services, and across all regulating/maintenance services the 1,078 units are 78% of the aggregate total. The cultural services add up to 218 units, making up the remaining 17% of services provided by woodland (Figure 4). This is important, since each of the individual services may not have an appropriate condition indicator (see Section 2.3 and Figure 5).

Figure 4. The distribution of the % ecosystem service delivery (weights), for each of provisioning, regulating/maintenance, and cultural service groups for each of the seven Broad Habitats. All columns total 100.

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Figure 5. The indicators used for in the calculation of the Woodland NCAI sorted by provisioning, regulating/maintenance, and cultural service groups, and aligned with the most relevant service within a group. Note that for many specific services there is no specific indicator. (NB. Two regulating/maintenance indicators are not attributed to a specific service here: Woodland Site Condition Index and Area of Certified Forest.) The latter is also used as a cultural service indicator. 2.3

The weighting of condition indicators within ecosystem service groups and Broad Habitats

The indicators of ‘condition’ of an ecosystem should ideally reflect the capacity of the Broad Habitat to deliver ecosystem services. However, since the choice of condition indicators was limited by the availability of pre-existing data5, there was no one-on-one match of an indicator for each individual ecosystem service: some had none, while others two or more (e.g.: Woodland: Figure 5). Thus the 100 ‘condition’ indicators were brigaded to reflect their relevance to ecosystem service groups in each Broad Habitat.

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Including information on bird and butterfly population data, site condition monitoring, Countryside Survey,  pollution  records,  and  other  physical  measures  such  as  volume  of  pesticide  use,  salmon  catch  and  timber  production, etc. 

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Figure 6. The number of ‘condition’ indicators used within provisioning, regulating/maintenance, and cultural service groups in each of the seven Broad Habitats. Each of the ‘condition’ indicators associated with an ecosystem service group of a Broad Habitat was assigned a relative weight, so that collectively all the indicators within that particular ecosystem service group summed to the net weight (percentage value) of that service group (Figure 7). Thus, in the case of Woodland, the nine indicators relevant to regulating/maintenance services add up to 78, the percentage total for this ecosystem service group. Similarly the six cultural service indicators add up to 17, reflecting the percentage total for this ecosystem service group. Finally since there was only one indicator of provisioning services, this assumed the total ecosystem service group value of 5%. The relative weights of the indicators was based on ‘expert’ judgement of the data quality and impact of change on ecosystem services, thus reflecting the perceived importance of a particular indicator for its ecosystem service group within a Broad Habitat

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Figure 7. The indicators of woodland condition were brigaded to provisioning, regulating/ maintenance, and cultural service groups. The ‘Weight’ column shows the importance attributed to that indicator in terms of its relative contribution to the delivery of that ecosystem service group. The weights of indicators within an ecosystem service group total to the net weight of that particular group of ecosystem services derived from the hierarchical method illustrated in Figures 2, 3 & 4.

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3. 3.1

THE EVALUATION OF THE CURRENT SUITE OF NCAI INDICATORS Background

The SNH choice of ‘quality’ indicator was based partly on relevance, and partly on regularity of collection. However, in many instances data availability was limited and ‘proxy’ indicators have been used. The technical document outlining the method comments that “Trying to identify indicators for environmental quality in the various Broad Habitats has highlighted the absence of information about much of our natural environment” (SNH 2012b). Currently there are 100 indicators of quality used in the NCAI, including: Site Condition Monitoring data, Countryside Survey data (e.g.: no. butterfly food species in broad-leaved woodland), pollution records and other physical measures (e.g.: volume of pesticide use), bird population data, salmon catch data, timber production, measures of visitor use, etc. 3.2

Development of a framework for evaluation of indicators

The systematic evaluation of these 100 indicators used across the seven Broad Habitats (ecosystems) considered in the NCAI was based around an adaptation of a set of criteria used previously to evaluate the appropriateness of biodiversity indicators (Biodiversity Indicators Partnership 2011). Seven criteria were selected against which the strength of evidence supporting the use of each indicator was judged (Figure 8), generally using a three level score (1=low to 3=high, with 0=unknown), and included:  the nature of the cause-effect relationship (0-2 only6);  whether the current use of indicator is sensitive to change in the asset;  the methodological soundness/transparency;  the data availability;  the frequency of updates;  the spatial coverage of Scotland; and  the potential for disaggregation.

Figure 8. The seven criteria used to assess the degree to which the NCAI indicators were ‘fit-for-purpose’. The levels of ‘fit’ (typically 0-3) within each of the criteria are described.

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Other than Unknown there were only two categories 1 = Theoretical but qualitative relationship, and 2 = Quantifiable relationship between the indicator and the integrity of the asset’s condition.

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Rather than sum the scores across the seven evaluation criteria, a ‘traffic light’ (green=fit-forpurpose, amber=possibly fit-for-purpose, red=not fit-for-purpose) was used to give a simple overview of the extent that the indicator was considered robust. This was done because some criteria were considered to be more crucial than others. For example, for Woodland the indicator ‘net annual change in carbon’ meets the top level on most of the seven criteria (see Figure 9) and is awarded a ‘green’ light. However, in contrast, the area of certified forest, which includes all Forestry Commission forests (currently 56% of Scotland’s forest stock), will only reflect small changes in accreditation given to other owners of forests. Thus, this indicator tells us nothing about the change in most of the nation’s forestry asset, hence we scored it 1 (i.e.: Does NOT detect change within scale of decision-making) in terms of sensitivity to change. Nor is it clear that the 20% decadal increase in area of certified forest reflects the capacity of the asset to deliver regulating services, so the cause-effect relationship was considered unknown (0), at best. However, although we had some reservations about re-using the same indicator, in this case the area of certified forest may reflect a perception by visitors of a well-managed forest, and therefore reflect a qualitative cause-effect (1) in terms of changes in cultural services. Nonetheless it would be better to try and ascertain if the public does make a distinction between certified and non-certified forest. Thus, overall we awarded the area of certified forest a ‘red’ light.

Figure 9. The scores for four of the criteria and the overall ‘traffic light’ of ‘fit-for-purpose’ from the evaluation of the 16 Woodland indicators. Key: for the evaluation criteria Figure 8; ‘green’= fit-for-purpose, ‘amber’=possibly fit-for-purpose, ‘red’=not fit-for-purpose, ‘brown’= Countryside Survey data with long intervals (8-9 years) between measures. The ‘brown’ light was awarded to all Countryside Survey data because although these may reflect direct measures of some aspects of the functional capacity of ecosystem assets, it’s not useful in an annual index because each is incorporated as an average change from 1998 to 2007, and then extrapolated from 2008 onwards at the same rate of change. However, like the ‘amber’ (possibly fit-for-purpose) traffic light, these Countryside Survey indicators would be worthy of consideration as part of a future systematic monitoring of natural capital assets, especially as they are easily disaggregated to smaller geographical scales.

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The evaluation criteria were first trialled for Woodland with all four members of the research team applying the criteria to each indicator, independently. This was done to highlight differences that might arise from our disciplinary and research backgrounds (two plant ecologists, one animal population ecologist and one economist), and to then try and harmonise “rules” for the application of the criteria. Each member of the research team reviewed three of the other six Broad Habitats, so that the set of indicators for each ecosystem was evaluated by two people, independently. The pairings of evaluators were designed so that each person worked with each of the other team members, and the same pair did only one Broad Habitat. Having undertaken the evaluation separately the “pairs” met to discuss differences and agree a common ‘traffic light’. The rationale for the agreed ‘traffic light’ colour for each indicator in each Broad Habitat can be found in Annex 2. 3.3

Results of the evaluation of the current suite of indicators

Although the proportion of indicators viewed as ‘green’ (fit-for-purpose) was disappointingly low (< 30% for all Broad Habitats), there were a large number viewed as ‘amber’ (possibly fit-for-purpose) (Figure 10). Typically concerns in the ‘amber’ class were around the extent to which changes in the indicator reflected changes in the asset (cause-effect relationship). Where indicators were scored ‘red’ (not fit-for-purpose), this was commonly because changes in the indicator were probably confounded by factors other than changes in the asset (particularly common for cultural indicators), or data was not sensitive to change, and/or had been extrapolated over periods of more than five years (e.g.: Countryside Survey data). The latter was considered a fatal flaw for an annual NCAI index, unless the indicator had a strong cause-effect relationship reflecting the functional capacity of the asset. In this case it might be considered as ‘amber’, if similar data could be acquired more regularly.

Figure 10. ‘Traffic light’ pie-charts showing the proportion of indicators in each Broad Habitat assessed as ‘green’ (fit-for-purpose), ‘amber’ (possibly fit-for-purpose), or ‘red’ (not fit-forpurpose). In these assessments the Countryside Survey data indicators are shown as ‘red’ even though they may be particularly relevant if they were collected more regularly.

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4.

THE EFFECTS OF EXCLUDING THE ‘RED’ INDICATORS ON THE NCAI

Clearly, unless there is strong concordance between the eleven or more indicators used within a Broad Habitat, then which ones are actually used could have an influence on changes in the habitat specific NCAI. Consequently, we explored the effect of excluding the ‘red’ indicators (not fit-for-purpose), as a guide to the ‘sensitivity’ of the original NCAI within each Broad Habitat (Figure 11).

Broad habitat

Figure 11. The original NCAI for each Broad Habitat from 2000 until 2010 (from Blaney and Fairley 2012). 4.1

The ‘sensitivity’ of the NCAI to the inclusion of indicators

In general, it seemed that excluding the ‘red’ indicators made little difference to the decadal trends within a Broad Habitat. In Woodland, Moorland, Freshwater and Coastal habitats, not only were the trends over time similar but the magnitude of the temporal changes were similar regardless of whether the ‘red’ indicators were included, as in the original NCAI, or excluded (Figure 12). Furthermore, when averaged across all Broad Habitats to give a Scotland NCAI, the differences were never more than 2% (Annex 3). Broad habitat Cropland Grassland Moorland Woodland Freshwater Greenspace Coast

Changes compared against original NCAi An increase from 2003-2007; no significant change in other years Significant fall from 2001-07, then a mild increase until up to 2011. No significant change (more or less stable) No significant change (more or less stable) No significant change (more or less stable) Significant lower for most of decade compared to the original NCAi Increased (but not that much)

Figure 12. Summary of changes in the NCAI when excluding the ‘red’ indicators compared with the original set used for each Broad Habitat (see Figure 13 and Annex 3 for details).

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However, substantial differences in the magnitude of fluctuations were seen in three Broad Habitats (Annex 3). For example, in Grassland the decline over the first five years was more pronounced when the ‘red’ indicators were excluded, followed by an equally more marked recovery to 2011, than when the original set of indicators was used (Figure 13c). The differences appeared to be associated with the inclusion/exclusion of indicators based on Countryside Survey data: when included, and averaged across years, the effect was to smooth out the annual changes. In contrast, in ‘Greenspace’ (urban parks, etc.), excluding the ‘red ‘indicators reduced markedly the change in NCAI (Figure 13d). Here the strong increase using the original indicator set appeared to be driven by measures of use (the Omnibus survey), which may have little to do with changes in the integrity of ‘Greenspace’ per se. In conclusion, since in some Broad Habitats the NCAI appears to be dependent upon the indicators used, care needs to be taken in their selection, especially if changes in the NCAI are the basis of management decisions.

Figure 13. The relative effect of excluding the ‘red’ indicators compared to the full set (original) indicators on the NCAI for a) Woodland, b) Cropland, c) Grassland, and d) ‘Greenspace’.

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5.

THE INFLUENCE OF CHANGING THE WEIGHTINGS ON THE NCAI

5.1

Rationale

As described in Section 2, the methodology of calculating the NCAI within Broad Habitats requires a system of weighting i) the Broad Habitat role in the delivery of ecosystem services, ii) weighting of the importance of ecosystem services to Scotland, and iii) the weighting of condition indicators within ecosystem groups of Broad Habitats. The methodology uses a range of quantitative and qualitative data. Many aspects of these weights were based on the judgement of experts, and sometimes quite small numbers of experts, or are drawn from Omnibus surveys, all of which can raise issues about the representativeness of the values attributed. For example, would another set of experts have given the same weight to a particular Broad Habitat in delivering a given ecosystem service, or attributed the same level of importance of that ecosystem service on a Scottish scale? Also, currently there is a limited understanding of the propagation of these values after multiplication across the various weighting matrices. Within the scope of this project it was not possible to undertake a full simulation modelling exercise of how changing the values at successive stages would lead to the NCAI changing. Nonetheless, it was considered worthwhile exploring how ‘sensitive’ the NCAI was to the weighting regimes. First, to changes in the distribution of the % ecosystem service weights for each of the provisioning, regulating/maintenance, and cultural service groups within each Broad Habitat (see Figure 4). And, second to a redistribution of provisioning weights, in particular, a halving of the percentage of all provisioning services per hectare attributed to Freshwater (89.1%), on the relative importance of Broad Habitats (see Figure 2) 5.2 5.2.1

Approach Ecosystem service group weights

We explored the ‘sensitivity’ of the NCAI to the relative weighting of the ecosystem service groups by considering both a ‘naïve’ scenario with 33.3% split across each of provisioning, regulating/maintenance, and cultural services, and a scenario that contrasted with the one actually used by SNH. The exercise was undertaken for an illustrative selection of four Broad Habitats (Cropland, Woodland, Grassland and Freshwater see Table 2). The outcome is illustrated in Figure 14. Table 2. The weightings used to explore the sensitivity of the NCAI in four Broad Habitats. Weightings of Ecosystem Service groups (Provisioning : Regulating : Cultural) Broad Habitat

5.2.2

NCAI original

Equal

Contrasting

Cropland

18:57:25

33:33:33

75:15:10

Woodland

5:78:17

33:33:33

25:30:45

Grassland Freshwater

3:79:18 68:22:10

33:33:33 33:33:33

25:45:30 20:60:20

Redistribution of provisioning weights on the relative importance of Broad Habitats

In this case we reduced the original 89.1% of the total provisioning service attributed to each hectare of Freshwater (based on the economic GVA values across all Broad Habitats - see

15

Figure 2 and Annex 1) to 45% and redistributed the other 44% equally between the other six Broad Habitats (7.3%). We then recalculated both the total ecosystem service delivery per hectare of each Broad Habitat and derived a new relative Broad Habitat weight (Figure 15). 5.3 5.3.1

Apparent ‘sensitivity’ of NCAI to the weightings Ecosystem service group weights

Generally, the trends in the NCAI for the four Broad Habitats we considered appear insensitive to changes in weightings of the ecosystem service groups (Figure 14). However, with the exception of Grassland (Figure 14c) the original weightings (blue lines) tended to generate the biggest temporal range over the 12 years. Interestingly our ‘contrasting’ scenario (green lines) consistently showed the most conservative temporal change over time. In some Broad Habitats the percentage values within a year varied by as much as 8% (range 86-94: Grassland in 2010 – Figure 14c), which could have significant implications for managers trying to decide whether intervention is necessary.

Figure 14. The relative effect of changing the weights of the full set (original) indicators on the NCAI for a) Cropland, b) Woodland, c) Grassland, and d) Freshwater. 5.3.2 Redistribution of provisioning weights on the relative importance of Broad Habitats The effect of halving the apparently skewed value of the percentage of all provisioning services in Scotland attributed to Freshwater (89.1%) on a per hectare basis, and redistributing the rest between the other six Broad Habitats, reduced the relative importance of Freshwater from a multiplier of 5.0 to 2.6 (compared to Cropland – the least important for overall ecosystem services). However, the effect on the other Broad Habitats was very

16

marginal (Figure 15). Furthermore, since the area of Freshwater is comparatively small (6.5% of Scotland), the halving of its provisioning weight has very little effect when multiplied through to derive an aggregate Scotland-wide NCAI. Consequently, it seems that given the final weights were derived after multiple steps of normalization and aggregation, the influence of any one individual indicator in either the Broad Habitat or Scotland-wide NCAI is ameliorated. This is a potential strength given that often for provisioning services there was only one indicator available. Nonetheless we think that since most of the weightings were based on the judgement of a few experts/stakeholders, there is a need to revisit the assumptions influencing the relative weightings. If some of the judgements that derived the values were viewed as contentious then this might undermine confidence in using the NCAI.

Figure 15. The effect of halving the % provisioning services per hectare attributed to Freshwater and redistributing the remainder equally between the other Broad Habitats. This changes the total ecosystem services delivered and hence their weights relative to the smallest (Cropland).

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6. 6.1

RECENT DEVELOPMENTS IN ASSESSING CHANGE IN NATURAL CAPITAL ASSETS Background

Around the globe, there is widespread interest in assessing the state and trends in both the stock and quality of natural capital assets, since these underpin the sustained delivery of ecosystem services, and hence influence economic and non-monetary well-being (MA 2005, TEEB 2010, UK NEA 2011). However, as England’s Natural Capital Committee (NCC) has pointed out, the range of metrics currently used as an evidence base are typically a mix of stocks, flows, quality and benefits – which are not necessarily the appropriate metrics for accounting for natural capital within policy frameworks aimed at sustainable development (NCC 2013, NCC 2014). The NCC is recommending that the Office for National Statistics (ONS) meet the challenge of developing robust methods of ecosystem accounting, through its collaboration with the European Commission (EC), Organisation of Economic Cooperation and Development (OECD), the United Nations (UN) and the World Bank (WB) (UNCEEA 2013). An accounting framework enables the stock of ecosystems – ecosystem assets – and flows from ecosystems – ecosystem services -to be defined in relation to each other, and also in relation to other environmental, economic and social information. Since the system of ecosystem accounting is a relatively new and emerging field of measurement, the work is deliberately labelled “experimental” ecosystem accounting. The ONS has recently published a description of its methods to achieve partial monetary valuation of natural capital using the framework developed by UNCEEA (ONS 2014). 6.2

Experimental Ecosystem Accounting - The SEEA 2012

The System of Environmental-Economic Accounting 2012 – Experimental Ecosystem Accounting (SEEA-EEA) tabulates non-monetary information about natural capital based on measurement of changes in both ecosystem extent, typically a land cover (the Broad Habitats in the NCAI), and the condition of that ecosystem (Broad Habitat). The product of the extent and condition of an ecosystem provides a means of tracking changes in the natural capital asset over an accounting period (a year in the case of the NCAI). The SEEA-EEA recommends that changes in ecosystem condition are measured in terms of indicators representing a suite of relevant key characteristics/ecological processes (water, soil, vegetation, biodiversity, carbon, nutrient cycling, etc.), with the aim of providing an overall assessment of the on-going functioning and integrity of the ecosystem asset (Figure 16). The criteria advocated for selecting appropriate indicators is that they are responsive to changes in the functioning and integrity of the ecosystem as a whole, over the accounting period. As appreciated when the NCAI was developed, a fundamental problem is the limited availability of suitable data on relevant key characteristics/ecological processes to detect change in the functional capacity of Broad Habitats. As a result, many of the current NCAI indicators reflect changes in the flow of ecosystem services, which may or may not reflect the capacity of a Broad Habitat to sustain the delivery of those services. Where we were more confident that indicators reflected the integrity of the Broad Habitat to deliver ecosystem services, the usefulness was often compromised because of long intervals between sampling, for example the Countryside Survey data. In this case the annual rate of change between the 1998-2007 surveys, has been extrapolated across the period 2000 to 2011 inclusive, and therefore is effectively only able to describe long-term decadal change, and clearly nothing is known about what actually happened after 2007. We return to these issues when discussing the options for refinement of the NCAI, in the next section. The SEEA-EEA approach also recommends a separate accounting table for ‘expected’ ecosystem service flows. Clearly, estimates of expectations not only require an

18

understanding of the factors determining the delivery of the current suite of ecosystem services, but also an understanding of the impacts of changes in ecosystem condition, and extent, on the capacity to deliver those ecosystem services in the future. In many cases this is a considerable challenge for researchers, and the basis for on-going research programmes, including the NERC programme Biodiversity and Ecosystem Service Sustainability.

Characteristics of ecosystem condition

Woodland

Vegetation

Biodiversity

Soil

Leaf Area Index (LAI),  biomass, etc

Spp. richness,  relative abundance

Soil organic matter  (SOM), groundwater

Water River flow, water  quality, fish spp. 

Carbon Net carbon balance,  primary productivity

Opening condition Improvements in  condition Natural  regeneration Human activity Reductions in  condition Extraction and  harvest On‐going  human activity Catastrophic  losses Closing condition

Figure 16. A schematic tabulation of the changes in condition of a Woodland ecosystem over an accounting period (e.g.: a year) suggested under the Experimental Ecosystem Accounting framework (UNCEEA 2013). 6.3

Natural Capital Asset Check

Recognising that there are significant data gaps for many natural capital assets and imperfect knowledge about the uses to which those assets might be put in future, the NCC has proposed the development of a risk register for natural capital assets which would assess the implications of excessive depletion (or a lack of restoration) systematically against a set of specific criteria (NCC 2014). This work, taken forward as part of the UK National Ecosystem Assessment Follow-on (NEAFO) project, has explored methods for a Natural Capital Asset Check (NCAC). The UK NEAFO recommends the adoption of an NCAC that can assess how much natural capital asset we have; its condition; what it produces; and how our decisions affect stocks, condition and flows of services over time.

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The NCAC conceptual framework explicitly defines a Natural Capital Asset as, “The configuration (in time, space, functionality and/or with other capital) of natural resources and ecological processes that contributes through its existence and/or in some combination to human welfare” (see Figure 17). Analysis of natural capital using this definition requires economics to use a holistic approach that takes account of ecological properties, and to look at how parts of ecosystems combine to produce services. For example, looking at intertidal ecosystems, we can identify their role as natural capital through different property combinations: along with populations of the fish species for which they provide nursery habitat, they form natural capital supporting fish stocks. Together with adjacent habitats (e.g. freshwater and sub-tidal), they form natural capital that supports recreation.

Figure 17. A schematic representation of natural capital, showing how component parts of ecosystems are recognised as interacting in productive configurations with other types of capital to influence well-being. The NCAC focuses on the relationship between the productivity of an asset in terms of the capacity to deliver ecosystem services and the integrity of the asset (Figure 18). The NCAC approach recommends that the uncertainties in the available evidence, likelihood of thresholds, and sustainability of the natural capital asset are classified.

20

Figure 18. A schematic representation of the relationship between the productivity of a natural capital asset and its integrity (extent x condition), showing ‘red flags’ as warnings of thresholds and the potential consequences of crossing them. The lack of quantification of these flow and stock relationships is a major gap in our evidence base which compromises decision-making.

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7.

OPTIONS FOR THE REFINEMENT OF THE NCAI

7.1 7.1.1

Summary of the evaluation of indicators and weighting system used in the NCAI Indicators

In our view one of the most significant issues with the NCAI is the fact that very few of the indicators truly reflect changes in ‘condition’ of a natural capital asset (the functional capacity/integrity of a Broad Habitat), and therefore the implications for the sustained delivery of a suite of ecosystem services are largely unknown. Unfortunately, those indicators that do reflect functional capacity, like many of those derived from Countryside Survey data, are only available periodically (every 8-9 years). Instead, many of the available indicators gathered more frequently are often only ‘proxy’ measures reflecting the delivery of a somewhat random selection of provisioning, regulating and cultural services. However, what the NCAI currently does do is effectively aggregate within and between all Broad Habitats to dynamically capture the trends in the flows of a bundle of ecosystem services. Therefore, the NCAI is a useful index of ‘actual’ ecosystem service flows, rather than the potential capacity (productivity) of natural capital assets to sustain the delivery of ecosystem services. It this sense it is a helpful summary of one of the most complex outputs of the UK NEA (see Figure 19).

Figure 19. The relative importance of Broad Habitats in delivering ecosystem services and overall direction of change in service flow since 1990 (from the UK National Ecosystem Assessment (2011)). 7.1.2

The weighting system

A major strength of the current NCAI is its basic hierarchical structure which allows one to amalgamate measures of change in natural capital asset indicators across a range of ecosystem services within a Broad Habitat (ecosystem) and then across Broad Habitats to give a single value for Scotland’s Natural Capital Asset (or as suggested above, an aggregate measure of the delivery of a bundle of ecosystem services). However, concerns have been expressed about the rationale of the relative weights assigned to ecosystem 22

services between and within provisioning, regulating and cultural services groups, as well as, the relative weights of Broad Habitats (Hambrey and Armstrong 2010). Our exploration of the ‘sensitivity’ of the NCAI showed that quite radical changes in the relative weights of ecosystem service groups may have little effect on the magnitude and trends in the index. Nonetheless, one needs to bear in mind that the changes are measured in percentages, so in absolute terms the differences may still be significant, and there is a risk that seemingly small variations could have important implications, particularly if the asset was close to an ecological threshold. Therefore, it would seem appropriate to consider re-examining the weightings with one or more groups of ‘experts’ across a range of disciplines and management backgrounds. 7.2

Future directions

There are essentially four areas of development work which could be undertaken to refine the NCAI and make it more fit-for-purpose. First, more thought needs to be given to the removal of problematic indicators. Second, alternative measures of key properties, and/or ecological processes that better reflect the functional capacity of natural capital assets need to be considered. Third, assuming the development of new indicators of the functional capacity of natural capital assets, it would be useful to re-consider the need for weighting across ecosystem service groups. Fourth, we should consider the appropriate interval for the collection of indicators of functional capacity and the update of the NCAI. It is proposed that these issues will be taken forward by joint working of the Natural Capital and Science and Technology groups of the Scottish Biodiversity Strategy. Nonetheless some preliminary thought was given to the second and fourth issues during this evaluation. For example, in terms of the indicators of the functional capacity, this is embedded in the System of Environmental-Economic Accounting 2012 – Experimental Ecosystem Accounting, which suggests measuring a variety of functional characteristics of ecosystem condition including vegetation (e.g.: primary production), soil (e.g.: soil organic matter), water (e.g.: quality ), among others (UNCEEA 2013; see also Figure 16). Unfortunately, our review of the current potential for additional/alternative indicators relevant to Woodland, and therefore potentially available in any revision of the NCAI for this Broad Habitat, was not particularly promising (see Annex 4). However, a way forward would be to focus on establishing the best measures of the functional capacity of natural capital assets to sustain the delivery of ecosystem services. Here there is an opportunity to review the new Ecosystem Health Indicators (Figure 20) proposed under the 2020 Challenge for Scotland’s Biodiversity (Scottish Government 2013). Of particular relevance are the plans for both the Freshwater Monitoring Plan and the Soil Monitoring Action Plan under the proposed CAMERAS Scottish Environmental Monitoring Programme. An advantage of linking the NCAI to the Scottish Environmental Monitoring Programme and, in particular, the Ecosystem Health Indicators, is that it should provide exactly the type of data recommended by the Experimental Ecosystem Accounting system (UNCEEA 2013). Furthermore, such data can be disaggregated at scales where local intervention could potentially restore degraded natural capital assets and enhance productivity. Furthermore, comparisons of site specific measures of both ecosystem service flow and functional capacity from multiple sites within ecosystems (Broad Habitats), would enable the shape of the relationship between productivity and integrity of natural capital assets (see Figure 18) to be determined, including the existence of thresholds, as envisaged in the Natural Capital Asset Check. Finally, there is a need to decide over what interval (annual, biennial, every five years, etc.) the NCAI should be collated. Some indices are likely to change slowly but where there are potential thresholds (the approach to which one needs to ‘flag’) there may be a need for a system of spatio-temporal sampling. So while one may only return to a specific site every five years, others might be monitored in the intervening years, and the rolling programme of measurement should be capable of detecting general trends.

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a)

Indicator Condition of components

1.

Habitat Quality and Condition

Source

Spatial Metric

Provider

EUNIS Habitat Maps

Habitat extent mapped by EUNIS category Condition of notified feature on protected areas Area and condition of woodland types HNV Characterisation To be determined To be determined To be determined Ecological Status

SNH

2

Site Condition Monitoring

3

National Forest Inventory

4

Extent of semi Natural habitat

5

Species Diversity

6

Ecological Status of Water Bodies Soil

7 b) 8

Function Fragmentation

High Nature Value Farming Bird diversity Notified species Species diversity Water Framework Directive Soil carbon

Habitat networks

SNH FC SRUC BTO SNH NBN SEPA

Soil carbon

James Hutton Institute

Indices of habitat connectivity Soil carbon

SNH

9

Carbon Sequestration

Soil carbon

10

Soil

Critical load Exceed

CEH/BGS

11

Habitat

Critical Load Exceed modelling of soils Critical load Exceedance of habitat

Critical load Exceedance

CEH/BGS

Extent of restoration action

SNH

Extent of selected INNS

NBN / DEFRA

(risk assessment maps – to be determined) Land Capability for Agriculture classes Soil erosion risk

To be specified

c) 12

Sustainability / Resilience Restoration

13

Invasive Non Native Species

14

Climate Change

Biodiversity Action Recording System (BARS) NBN/GB nonnative information portal ClimateXChange

15

Soil

Land capability Soil erosion risk maps

James Hutton Institute

James Hutton Institute James Hutton Institute

Figure 20. The candidate indicators being consider as Environmental Health Indicators as part of the 2020 Challenge for Scotland’s Biodiversity.

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8.

REFERENCES

Blaney, R. & Fairley, R. 2012. Valuing our ecosystems: Scotland’s Natural Capital Asset Index. In Agriculture and the Environment IX, Valuing Ecosystems: Policy, Economic and Management Interactions. pp 8–13. www.sruc.ac.uk/download/downloads/id/1394/813_fairley. CICES. 2013. Towards a Common International Classification for Ecosystem Services http://cices.eu/. Hambrey, J. & Armstrong, A. 2010. Piloting a Natural Capital Asset Index. Scottish Natural Heritage Commissioned Report No.750 http://www.snh.gov.uk/publications-data-andresearch/publications/search-the-catalogue/publication-detail/?id=2118. MA. 2005. Millennium Ecosystem Assessment. Ecosystems and Well-being: Synthesis. Island Press, Washington, D.C. http://www.maweb.org/en/Synthesis.aspx. NCC. 2013. The State of Natural Capital: Towards a framework for measurement and valuation. First report to the Economic Affairs Committee. http://www.naturalcapitalcommittee.org/state-of-natural-capital-reports.html. NCC. 2014. The State of Natural Capital: Restoring our Natural Assets. Second report to the Economic Affairs Committee. http://www.naturalcapitalcommittee.org/state-of-natural-capitalreports.html. ONS. 2014. The UK Natural Capital – Initial and partial monetary estimates. http://www.ons.gov.uk/ons/rel/environmental/uk-natural-capital/initial-estimates/index.html. Scottish Government. 2013. 2020 Challenge for Scotland’s Biodiversity: A Strategy for the conservation and enhancement of biodiversity in Scotland. ISBN 978-1-78256-586-4 http://www.scotland.gov.uk/Publications/2013/06/5538/downloads. SNH. 2012a. Scotland’s Natural http://www.snh.gov.uk/docs/B814140.pdf. SNH. 2012b. NCA index technical http://www.snh.gov.uk/docs/B1070304.pdf.

Capital document.

Asset V

1.0

(NCA) 12

April

Index. 2012

TEEB. 2010. The Economics of Ecosystems and Biodiversity. Mainstreaming the Economics of Nature: A Synthesis of the Approach, Conclusions and Recommendations of TEEB. http://www.teebweb.org/our-publications/teeb-study-reports/synthesisreport/#.Ujxmnn9mOG8. ten Brink, B. 2007. The Natural Capital Index framework (NCI). Contribution to Beyond GDP “Virtual Indicator Expo“. http://unstats.un.org/unsd/envaccounting/seeaLES/egm/NCI_bk.pdf. Turner, R.K. & Daily, G.C 2008. The Ecosystem Services Framework and Natural Capital Conservation. Environmental and Resource Economics, 39: 25-35. UK NEA. 2011a. UK National Ecosystem Assessment. Synthesis of the Key Findings. UNEP-WCMC, Cambridge. http://uknea.unep-wcmc.org/Resources/tabid/82/Default.aspx. UK NEA. 2011b. UK National Ecosystem Assessment. Technical Report. UNEP-WCMC, Cambridge. http://uknea.unep-wcmc.org/Resources/tabid/82/Default.aspx. 25

UNCEEA. 2013. System of Environmental-Economic Accounting 2012 – Experimental Ecosystem Accounting. http://unstats.un.org/unsd/envaccounting/eea_white_cover.pdf.

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ANNEX 1: THE ECONOMIC VALUE SOURCES USED TO CALCULATE THE RELATIVE CONTRIBUTION OF BROAD HABITATS TO THE DELIVERY OF PROVISIONING SERVICES  Coast including dunes, cliff beach and tidal mud flats:  the value of seaweed harvesting is  estimated at £800k a year (based on current industry data and an economic study from the mid‐ 1990s); and the value of hand‐picked and tidal shellfish is estimated at £1.1m a year (review of  literature). Materials and ornamental products (sand, pebbles, driftwood, and shells) are  estimated to be worth £100k a year. This gives a value of £2m/year.   Cropland including arable and intensive grass: a value of food is derived by considering the  relevant agriculture land use data reported in Scottish Government statistics. However, not all of  the £792m GVA is used since 22% is pigs, poultry, etc. (which, due to the nature of production,  are human capital intensive and have minimal reliance upon Scottish ecosystems). Of this  adjusted GVA figure, 49% is based on the value of output under activity headings crops and dairy,  and is included in this broad habitat, along with one‐third of the value of unimproved grassland  livestock output to take account of livestock finishing on improved grasslands and feed transfers  from cropland. The value of pollination services equivalent to £41m (adjusted UK NEA figure) are  deducted as this value is assigned under a different ecosystem service heading. This gives a final  value of £300m/year.   Grassland including rough grazing and semi‐natural:  the agricultural GVA is used above as for  cropland and intensive grass, with 29% under activity headings cattle and sheep, which is reduced  to take account of values assigned to cropland and intensive grasslands (livestock finishing and  feed) and moorland (grazing). This gives a value of almost £80m/year.   Moorland including montane, peatland & bog: one‐sixth of the value of unimproved grassland  livestock output (£19m) is used to take account of livestock grazing on moorlands, along with  £1m estimated from peat extraction (UK National Ecosystem Assessment value reduced by 50%  to estimate GVA), and £1m from wild venison (UK NEA average between 2002‐2009 of £3m total  wild venison wholesale value, reduced by 50% to estimate GVA; two‐thirds assigned to moorland  one‐third to woodland to account for deer movements between these habitats). The net value of  this habitat for honey production is estimated at £1m, giving a total value of £22m/year.   Woodland including commercial forestry: £106m GVA (from ‘The economic and social  contribution of forestry for people in Scotland’ ‐ research note Sep 2008) is used; added to this is  £5m from Christmas tree production, £0.4m from woodland moss collection, and £0.04 million  from wild mushroom and fruit harvesting, as well as £0.5m from wild venison (various sources –  see also heather moorland). This gives a value of approx £110m/year.   Freshwater including lochs, rivers and fen/mire wetland: a water value is derived from Scottish  Water’s gross surplus (i.e. revenue minus cost of sales 2010, taken from the consolidated income  statement in the annual report) of £450m. A further deduction of £20m (from literature reviews)  is made to take account of water quality functions assigned to other broad habitats. Add value of  water taken directly from environment via boreholes, springs etc. (agriculture, distilling, and  paper industries only) of £90m. This gives a value of £520m/year. Freshwater fishing is assigned  to cultural services rather than provisioning services, as the prime intention is recreational.   Greenspace: the value is based on production in gardens and allotments. Vegetable seed sales  across the UK in 2010 were £60m, with Scotland assigned 1/12th of this based on population, and  thus £5m. It is assumed that the average value of production is six times the cost of seeds, £30m.  Labour and equipment costs (i.e. depreciation value of spades, watering cans, etc.) are deducted,  estimated at £20m (labour at £17m and equipment depreciation at £3m a year). The seed cost is 

27

also deducted. A further deduction of 10% of value is made for pollination services. This gives a  rounded value of £5m/year. 

28

ANNEX 2: THE RATIONALE FOR AGREEING THE ‘TRAFFIC LIGHTS’ FOR THE INDICATORS IN EACH BROAD HABITAT Croplands ‐ final colour coding Potential for provisioning services ‐ Grazing potential (area of seeded grass under 5 years old)

Arable land capability (5‐year yield plus soil fertility score)

Potential for regulating & maintenance services ‐ Bare fallow/set‐aside area

Fertiliser use (inverse)

Pesticide use (inverse)

Final comments Grazing potential as an indicator seems ok. But the problem is the way it is measured. Because, the general  formula for the NCAi is: quantity x quality, where the 'quantity' is represented by the area and 'quality' by the  capacity of Broad Habitat to deliver ES. However, for this particular indicator, quality is also expressed by 'area',  whcih makes the measurement of this indicator inconsistent with the underlying formula of the NCA index.  Relates to the service and not the assest; unsustainable agricultural practices may produce high yields but  erode the capital (soil quality and stability). A more direct indicator of the asset is required e.g. a soil health  indicator (pH, soil organic carbon, compaction)

Noticeable change in areas of setaside since 2007 due to CAP policy change where it no longer a requirement to  set a percentage of land receive the SFP. It is still a proxy indicator.  From the report where the data was drawn it implies that it is in tonnes of nutrients used per year rather than  an application rate per ha. It needs to be expressed as an average fertiliser application rate (per hectare) e.g.  increase in fertiliser use could mean that the area of arable has increased, rather than increased intensity of  use Detoxification demands will be directly related

Farmland bird index

Limited sampling in highlands and west coast, tenuous link to regulating ‐pollination (food sources). Possibly  better as a  cultural service indicator ‐ this would also avoid double‐ accounting

Hedges species richness

Based on Countryside survey data which is  collected every 10 years ‐ suitability for an annual index is  questionable. Somewhat tenious links to regulating service?

Species richness arable land 

These appear to good indicators, but the frequency of countryside survey limited its use for annual index. So,  dark amber!

Species richness improved grass

These appear to good indicators, but the frequency of countryside survey limited its use for annual index. So,  dark amber! Maybe forb ratio would be a better indicator for pollinators. 

Agri‐environment area

The assumption here that the agri‐envi. schemes improve biodiversity, landscape, etc. the evidence is not  conclusive. Butterflies are not key pollinators for crop species. Data from all species in Scotland is not sufficient to provide  a full picture. Maybe better as a cultural indicator. Be careful of double accounting. 

Butterflies ‐ generalists Mixed farming Soil carbon Potential for cultural services ‐ Hedges in the landscape (total length of hedgrows)

Mixed farming can be any combination of arable‐livestock agriculture. It doesn't show the intensity of farming  or farming management system. So it isn't an asset attribute.   Based on Countryside survey data which is  collected every 10 years ‐ suitability for an annual index is  questionable.  This seems a good cultural service indicator, but data is based on Countryside Survey; doesn't show annual  changes.  So, dark amber!

Lowland boundary walls in landscape (total wall length) Butterflies availability to watch ‐ generalists

Again a Countryside survey data set, so infrquent collection an issue Data from all species in Scotland is not sufficient to provide a full picture. Improved sampling coverage would  help. General trend stable but covers up sig. declines in some species

Birds availability to watch: farmland bird index

Generally accepted as a robust measure of arable croplands.

Farm animals: no. livestock in non‐LFA (cattle & sheep)

The link between number of livestock and cultural service provision is not known. How many cows is too many?  Or too little? And what about if all the cows were on Islay but now where else? This indicator needs to be backed up with evidence on the public dislike of polytunnels in the landscape. 

Amount of landscape covered in polytunnels (inverse)

Figure A1. The final ‘traffic light’ for Cropland indicators.

29

Grasslands ‐ Final colour coding

Combined comments

Potential for provisioning services -

Grazing - total no. Livestock Units in the LFA

Hard to see how this links to grazing outputs from grassland in general.  Better metrics may be available in the agricultural  census data. Hard to capture issues such as overgrazing, because in this context a greater number of livestock is considered  as having a positive effect on the index.

Potential for regulating & maintenance services -

Level of cattle grazing (total no. in the LFA)

Farmland & Upland birds (combined index)

Butterflies (specialists)

Area of hay meadow Level of sheep grazing in north west (no. ewes) Neutral grassland species richness

Festuca ovina+ Galium saxatile in acid grassland Grassland Site Condition (favourable condition)

The link is unclear but it might be to climate regulation, with cattle numbers likely to be linked to some extent to GHG  emmissions from farming, but again why use only livestock data from LFA?  In addition how does the negative effect of this  factor here trade off against its positive effect on the indicator through provisioning services, above? Not very clear that these two measures of bird abundance are really related to delivery of regulating services. Not least the  link to grassland assets seems very weak ‐ this index combines birds from a wide variety of habitats, only some of which are  gasslands. in addition the link from biodiversity (especially high trophic level diversity) and regulating functions could be  quite weak. This may be a better indicator of cultural services. Approach by which site data have been agrgegated to Scotland level is not clear. Based on volunteer effort in many cases, so  presumably there is spatial variability in recorder effort. Finally, not clear that butterlfy numbers reflect overall system  capacity for pollination, since many other insects may be involved, and their numbers might/might not be associated with  butterfly numbers. Note ‐ should be area of grass cut for hay, which is subtly different. Underlying assumption is that hay meadows promote  pollinating insects ‐ not a bad assumption. Doesn't seem relevant to a national‐scale indicator. Unclear why it is not included under provisioning. Works on the assumption that high N loads etc. would reduce species richness, which in turn would alter nutrient cycling  processes. Source data is Countryside Survey, so has good spatial but poor temporal resolution. Major problem is temporal  resolution and extrapolation between time points Hard to see what the abundance of this species in a particular grassland type tells you about regulating services in grasslands  overall. Is also Countryside Survey data. Limiting factor is the extent to which sites included in SCM are representative of grassland sites in general. Normally they  are protected areas and so might be unrepresentative.

Potential for cultural services -

Number of working occupiers in the LFA Farmland & Upland birds (combined index)

Neutral grassland species richness target plots

Area of hay meadow

Impossible to assess whether increasing or decreasing values for this metric would be good or bad for cultural services. In  addition the data is again only for LFA.  Better here than under regulating services, although the link to specifically grassland assets remains weak ‐ this index  combines birds from a wide variety of habitats, only some of which are grasslands, hence an amber rather than green rating.  Source data is Countryside Survey, so has good spatial but poor temporal resolution. Data availability depends on particular  requests, e.g. the need for fine spatial scale resolution on data points. Major problem is temporal resolution and  extrapolation between time points; also the issue of representativeness for grasslands overall. Note ‐ should be area of grass cut for hay, which is subtly different and perhaps more relevant here than under regulating  services, i.e. genuine hay meadow might have high cultural value than non hay grasslands cut for hay. No link given, but data  come from December agricultural census. Scorings are made on this basis. 

Figure A2. The final ‘traffic light’ for Grassland indicators.

Moorland ‐ final colour coding

Comments

Potential for provisioning services Grazing potential (no. moorland ewes & deer for venison) Potential for regulating & maintenance services Bracken encroachment (inverse) Upland bird index

Number of ewes on the hill is largely an artefact of changes in CAP and socio‐economic factors. Deer  populations are increasing due to ameliorating winter climate and/or reductions in sheep numbers. Countryside Survey ‐ so an extrapolation of the average of the annual trend between the last two  surveys (1998 ‐ 2007) Annual values but does it reflect regulating/maintenance surveys? Better as a potential cultural service  indicator.

Heath species richness Bog moisture score Bog grass:forb ratio (inverse) Heath butterfly food Soil carbon concentration in bogs

Countryside Survey ‐ so an extrapolation of the annual trend between the last two surveys (1998 ‐  2007) Countryside Survey ‐ so an extrapolation of the annual trend between the last two surveys (1998 ‐  2007) Countryside Survey ‐ so an extrapolation of the annual trend between the last two surveys (1998 ‐  2007) Countryside Survey ‐ so an extrapolation of the annual trend between the last two surveys (1998 ‐  2007)

Upland Site Condition (favourable) Potential for cultural services

Only conducted in 2005, 2010 & 2011

Birds of prey available for watching ‐ persecution (inverse)

Data is very specific and of questionable accuaracy. Seems unnecessary given the Upland bird index

Other birds available for watching ‐ upland bird index

Deer available for sport shooting ‐ number of deer shot

Probably a good measure of the attractiveness/recreation potential  Data is very specific applying to a minority, and highly variable between years due to spring weather.  Seems unnecessary given the Upland bird index Deer shot doesn't reflect the herd size or the carrying capacity. Given hinds are shot by local stalkers it  may have little to do with numbers of clients

Landscape - bracken encroachment (inverse)

Countryside Survey ‐ so an extrapolation of the annual trend between the last two surveys (1998 ‐  2007). Also, is there any evidence that people perceive bracken as a problem and sign of degradation

Landscape ‐ impact of windfarms

Data from just 2002 and 2008

Birds available for sport shooting ‐ red grouse numbers

Figure A3. The final ‘traffic light’ for Moorland indicators.

30

Freshwater ‐ Final colour coding

Combined comments

Potential for provisioning services ‐ Available stock of water Raw water quality: nitrates in rivers at safe level Potential for regulating & maintenance services ‐ Fen, marsh, swamp species richness  Streamside species richness 

Doesn't measure supply of water just precipitation as it's a watercycle/supporting service. Based on three year  averaged data.  A regulating service. Confusion over data ‐ just nitrates or other water quality indices? Based on countryside survey data  (every 10 years) ‐ questionable use for an annual index. Not sure species  richness is a good proxy for regulating services of freshwater as the causal links are not known. Based on countryside survey data  (every 10 years) ‐ questionable use for an annual index. Not sure species  richness is a good proxy for regulating services of freshwater as the causal links are not known. Based on countryside survey data  (every 10 years) ‐ questionable use for an annual index. Not sure species  richness is a good proxy for regulating services of freshwater as the causal links are not known.

Pooled headwater plant species richness  Pollution: orthophosphate at safe level

Good measure of detoxification  The effect of abstraction on ecosystems is not fully known. Data not available for viewing

Raw water abstractions (inverse) Non‐native invasive species (inverse)

INNS only recently established ‐ unsure how data will be compiled to form an indicator. Current index is  invariant as it is. Going forward may be possible to use as an indicator.

River water quality (% unpolluted sites)

Is this not the same data as used for water quality indices? Possible duplication? Double accounting? 

Freshwater Site Condition (favourable condition) Potential for cultural services ‐

Somewhat selective in its choice of original sites (better quality ones with qualifying features)

Number of ponds Unsure which aspect of cultural services it represents. CS data ‐ every 10 years which explains linear trend. Headwater streams habitat quality assessment (HQA) Unsure of its relation to cultural services. Suspicious linear trends. CS survey data.  Only 2 data points (greater than 5 years). Not sure cultural services assets should be based on one mammal  Otter availability to watch: population which is known to be recovering but elusive? Integrity measure of salmon stock but not of freshwater. Could be improved by using fish count data rather  Salmon availability to watch/fish: population est  than rod catch data.  from catch

Figure A4. The final ‘traffic light’ for Freshwater indicators.

GREENSPACE ‐ FINAL COLOUR CODING

COMBINED COMMENTS

Ability to provide provisioning services

Land productivity ‐ garden/allotment food production

Data is not available on annual basis. Seems to be based on Omnibus survey of % adults producing  some of their own food but no measure of quantitites.

Ability to provide regulating & maintenance services

Local Authority areas covered by greenspace strategies

 If it is non‐roadside may be OK. But if roadside as seems likely from database then is probably a poor  reflection of the asset because cars have become more fuel efficient. Also, has NO2 deposition in non‐ green urban areas been factored?  Looks very dodgy given exponetial rise in last two years. Not clear it reflects regulating services  capacity. Data is not on annual basis, but potential to have data on annual basis.

Urban bird (garden birds) abundance

Not a regulating service nor an index of maintenance but exclude here since also a cultural service.  Also, it is restricted to South Scotland.

Pollution ‐ urban background NO2 deposition

Ability to provide cultural services A place for children to play Provides a space to relax Attractive green areas A place to see nature Quality reduced in last 5 years

These indicators to a certain degree show flows of ecosystem services from the green space. However,  they are highly correlated and based on perceptions of individuals intervewed at a point in time.  No  evidence to relate the perception change to elements of the asset. All these omnibus surveys tells you  about perceptions of supply what are they really measuring about the stock?

Birds available to watch ‐ garden birds (urban)

Good indicator, but appears under regulating/maintenance services too.

Landscape ‐ amount of abandoned greenspace (inverse)

Concern that land previously vacant is lost to development It doesn't necessairly reflect the changes in asset; it may be due to weather conditions. This tells us  about demand which may have nothing to do with integrity of stock.

Accessibility ‐ visits to urban parks

Figure A5. The final ‘traffic light’ for Greenspace indicators.

31

Coasts ‐ Final colour coding

Combined comments

Potential for provisioning services ‐ Stock of shellfish for picking (cockle biomass & quality) Fishery closed in 2011 and spatial coverage is limited to Solway firth Potential for regulating & maintenance services  Bathing water quality (guideline) Why not use mandatory data? Wintering waterbird index Causal links between waterbirds and regulating services too tenious  There are too many factors governing the  numbers of breeding birds on coasts, water quality being only one of them. Non‐native invasive species (inverse) Further clarification of methodology/data to be used is required: it might be possible to disaggregate the data but  with limitations with respect ot variable recorder effort. Likely to be increasing number of datasets relevant to  this type of indicator. Limiting factor is the extent to which sites included in SCM are representative of coastal sites in general. Normally  Coastal Site Condition (favourable condition) they are protected areas and so might be unrepresentative. In addition there are currently only two data points ‐  2005 and 2010  ‐ but SCM is switching to a rolling programme . Erosion (sea level rise) Only two time points and sea level rise may not be the only factor for coastal erosion.  Potential for cultural services ‐ Bathing water quality (mandatory) Good quality transparent methodology collected annually Coastal birds Issue with the integrity of some of the data e.g. eagle data; decision re relative weighting of different data types  is also unclear. Beach litter count (inverse) Further exploration of the methodology is required to ensure consistency between surveys. MCS beach quality measure Based in SEPA data and so a duplicate indicator. They do include an additional category ‐ recommended ‐ which is  a useful addition. Use of marked coastal paths This is more of a service or usage indicator rather than assest. Maybe something like the number of miles of  accessible coastline, or state of coastal footpaths would be more appropriate.

Figure A6. The final ‘traffic light’ for Coastal indicators.

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ANNEX 3: COMPARISON OF THE NCAI WITH AND WITHOUT THE ‘RED’ INDICATORS Broad Habitat

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Cropland original Excl. red indicators

100

99

100

103

104

107

108

105

99

100

100

101

100

101

102

106

106

112

113

109

99

101

99

101

Grassland original

100

94

90

88

87

88

91

92

94

94

94

91

99

90

80

80

76

81

90

91

95

99

104

101

Moorland original

100

100

99

97

96

97

96

95

96

95

95

94

Excl. red indicators

100

100

100

96

95

96

95

95

97

96

95

96

Woodland original

100

101

103

105

106

105

105

104

104

103

100

102

Excl. red indicators

100

101

104

106

109

107

107

105

105

104

99

100

Freshwater original

100

97

98

95

102

104

108

109

111

112

105

109

Excl. red indicators

99

96

98

94

103

105

109

111

112

113

106

110

Greenspace original

100

101

105

106

107

110

112

115

119

118

115

115

Excl. red indicators

100

96

104

103

101

100

101

102

104

105

105

110

Coast original

100

104

106

119

120

120

121

115

115

116

112

116

Excl. red indicators

100

104

106

121

121

122

123

116

117

120

116

119

Scotland NCAI (org)

100

99

99

98

98

99

100

99

99

99

98

98

Excl. red indicators

100

98

97

97

97

99

100

100

100

100

99

100

Excl. red indicators

Figure A7. The NCAI for each Broad Habitat, and also aggregated for Scotland, comparing the original values calculated using all the indicators and then excluding the ‘red’ indicators for each year 2000 to 2011.

33

ANNEX 4: THE POTENTIAL FOR ADDITIONAL/ALTERNATIVE INDICATORS IN A REVISION OF THE NCAI In this section we look at the potential for the use of additional and/or alternative indicators in the NCAI, in particular for those assets for which there are currently either no, limited or poor indicators. To explore this potential we focus primarily on indicators relating to woodland habitats. Also, we consider some additional inputs for urban (green-space) and enclosed farmland (cropland in the NCAI). However, as we will see it is likely that these findings will be relevant to the other Broad Habitats. A3.1 The Ecosystem Services Indicator database The Ecosystem Service Indicator (ESI) database is one of the identified outputs of the Ecosystem Services Theme of the Scottish Government Research Programme, Environmental Change (2011-2016). Like the NCAI, the database is organised according to Broad Habitats. For example, ‘Woodlands’ in the ESI database comprises broadleaved, mixed and yew woodlands as well as coniferous woodland. In addition, for policy relevance, fields within the database relate each indicator to a broad policy objective within the Scottish Land Use Strategy (Scottish Government, 2011) such as, for example, a ‘low carbon economy’ or ‘sustainable water management’. Although the main purpose of the ESI database is to review and identify potential ecosystem service indicators, with a focus on those which relate directly to goods and benefits, there are synergies with the purpose of the NCAI, in that, through the ecosystem services cascade (Haines-Young and Potschin, 2010, see Figure A8 below), it attempts to identify indicators of ecosystem function. These indicators of ecosystem function relate to the integrity and quality (condition) of the ecosystem or, in other words, natural capital. It is noteworthy that the ESI database includes indicators across a range of scales, from individual fields/plots to regional and national scales.

Figure A8. A simplified framework of relationships between ecosystem services and benefits. Adapted from Haines-Young and Potschin, 2010.

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A3.2 Potential woodland/ urban greenspace tree indicators In reviewing the ESI database for woodlands and filtering out potential indicators, only those indicators which directly assessed the condition (quality) or extent (quantity) of the natural asset and scored highly against the criteria used in the indicator evaluation framework were selected (e.g. only those that showed a cause-effect relationship, or where data were collected over a range of spatial scales etc. see chapter 2). A review of the database revealed five potential indicators, which are listed in Table A1 along with information on monitoring frequency, data sources, comments and traffic light. Two of the woodland indicators scored ‘green’ traffic lights, despite monitoring frequencies of every five years. First, the number of households (only settlements > 500 people) with visible woodlands, within both 1 km and 300 metres, is an indicator identified by Edwards et al. (2009) in a report on the social and environmental benefits of woodlands to Scotland. The indicator uses the data from the NFI and combines it with a “view shed” GIS analysis to calculate how many households had a view of woodland within both a 1 km and 300 m radius of their house. This indicator could be further refined as currently it does not distinguish between the different types of woodland, for example, coniferous plantations or native broadleaved woodlands, that are visible from settlements. National surveys (e.g. FC Public Opinion Surveys), choice experiments (Willis et al., 2003) and evidence from local case-studies indicate that people have a preference (in terms of landscape aesthetics) for native, mixed species and open woodlands. This could be seen as evidence for quality attributes that contribute to the natural asset of woodland. The second indicator that scored a ‘green’ traffic light was the area of broadleaved/native woodlands, mixed woodland and open space available from the NFI. This is similar to the existing NCAI indicator from Countryside Survey (CS) data - the area amount of broadleaved woodland in Scotland. However, the NFI is collected on a rolling basis every 5 years, rather than the 8-9 years of CS data, and also uses additional condition/quality attributes, such as native Scots pinewood, openness of woodland and mixed species woodland. Three of the indicators, amount of riparian woodland, woodland recreation opportunities, and visitor use of woodlands, all scored an ‘amber’ traffic light. The amount of riparian woodland is a very good indicator of the potential of natural capital to attenuate flood risk and improve water quality. However, there needs to be further exploration of the quality and frequency of the data collected by the Native Woodland Survey of Scotland (NWSS) and whether NFI data will distinguish riparian woodlands separately from broadleaved woodlands. The other two ‘amber’ indicators related to the supply and demand for recreation. First, the supply of woodland recreation opportunities is based on a biennial public opinion survey by the Forestry Commission. With this indicator, it is difficult to discern between, or draw links, to the quality (condition) of the natural asset and hence ranked values of performance. For example, particular woodland may score highly in terms of recreation opportunities, but this may not reflect the quality of the woodland asset, representing instead the quality of footpaths or the children’s play park, both infrastructure assets. This complication of interactions between natural capital and other types of capital (built infra-structure, human) has been recognised and explicitly incorporated into the conceptual framework for developing a Natural Capital Asset Check by the UK National Ecosystem Assessment Follow on project (Dickie et al. 2013; see also Figure 18 earlier in this report).

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Table A1

Potential woodland indicators from the ESI database

Indicator

Service

Frequency

Amount of riparian woodland

Flood attenuation/Water Quality

5 years

Woodland recreation opportunities (Very good, Good, Fair, Poor, Very poor, No experience/Don't know)

Recreation opportunities

2 years

Public Opinion Survey (FC )

No. of households (settlements > 500 people) with visible woodland

Landscape aesthetics

5 years

NFI with GIS view shed analysis (FR)

Amount of broadleaved/native woodlands, mixed woodland and open space No. of visits to Scottish woodlands, distance travelled to woodland, duration of visit

Aesthetics

5 years

NFI, NWSS

Recreation

Annual

The Scottish Recreation Survey

Table A2

Data sources NFI, NWSS

Notes

Traffic light

Will NFI have a separate report for riparian woodlands? How often is NWSS planned? Uncertainty regarding frequency of systematic monitoring. Could indicate combined asset rather than natural asset. Does not distinguish between local and Scotland wide woodlands. Requirement of FR to conduct view shed analysis. No distinction between the type of woodland e.g. conifer plantation of native broadleaf

The last year of the survey is 2013. No. of visits is a proxy measure of asset as asset may deteriorate with increased usage (erosion, dog fouling, litter)

Potential tree indicator for green space from the ESI database

Indicator

Service

Frequency

No. of street trees

Air pollution

?

Data sources ?

Notes

Traffic light

Could something like the English ‘Trees in Town II’ survey be commissioned? Or could data from councils be used? Could the Urban Tree Survey run by the Natural History museum provide future data?

Second, the demand for recreation, as indicated by the number of visits to a woodland or distance travelled, may reflect more about the proximity of urban concentrations of people, than the condition of the natural asset. Furthermore, an increase in the number of visits to woodland may actually lead to the deterioration of the condition of the asset, by means of erosion, litter or dog fouling. Therefore caution must be used (hence the amber traffic light score) in the application of these indirect indicators. 36

Although perhaps more commonly associated with green space, or urban areas, than woodlands per se, the number of street trees was identified as an additional metric in the current NCAI indicator portfolio (Table A2). While this potential indicator was identified from the ESI database, the data is based on the Trees in Town II (Britt & Johnston, 2008) survey which is restricted to England, and the frequency of future monitoring is uncertain. The potential for developing a similar indicator in Scotland would need further exploration to see whether current data sources exist (e.g.: local authority registers), or whether fit-for-purpose surveys could be commissioned to fill the substantial gap in NCAI indicators in our cities and urban spaces. 6.3 The search for a natural capital asset indicator for soils From the evaluation of indicators for enclosed farmland (cropland) and subsequent discussions with soil science experts at the James Hutton Institute (Willie Towers, Helaina Black and Jason Owen) it became apparent that a suitable indicator to measure the critical natural asset of our soils was missing from the NCAI. The key attributes of arable soils that indicate ‘asset quality’, or in other terms, soil health, would be soil pH, soil organic matter, aggregate stability and compaction. For example, arable crops will only grow in a very narrow pH range (5.9-6.4), while the level of soil organic matter is related to how well soil holds and filters water, an important regulating service with regards to flood risk attenuation and water quality (Towers, pers. comm., ). A review of the ESI database identified a number of potential indicators for monitoring soil health in arable ecosystems (see Table A3). However, on closer inspection and upon applying the evaluation criteria, they all score poorly, either in terms of monitoring frequency, spatial coverage or data availability. This evaluation concurs with the State of Scotland’s Soil report (Dobbie, Bruneau & Towers 2011) which, despite identifying 31 different monitoring, surveillance and data sources, recommended that there needed to be a concerted effort to tackle the lack of systematic spatio-temporal soil data. 6.4 Further challenges A review of the ESI database for woodlands identified a number of potential indicators that could be used to enhance the existing indicators used in the NCAI. However, as indicated by those with an ‘amber’ traffic light scoring there is still ‘room for improvement’, especially when considering complex combined assets commonly found in green space. From our findings in the woodland habitat it is anticipated that there may be additional opportunities for improving indicators, and for gap filling in the other Broad Habitats. However, as the current lack of suitable indicators for soil quality reflects, along with the large proportion of indicators in the NCAI evaluation that scored a red traffic light, there still remains a significant challenge in compiling a suite of indicators that directly assess the quality/condition of Scotland’s natural assets, and that have the spatial and temporal coverage necessary for an annual index of natural capital. The recently established CAMERAs environmental monitoring network, through its monitoring action plans (MAPs) such as the soil MAP, may in the next few years deliver more fit-for-purpose indicators of natural capital. However, this can be guaranteed only if these plans address the monitoring and data synthesis requirements for the NCAI as well. In addition, with increased citizen science and web-based tools, such as BeeWatch and Urban Tree Survey, there may be further monitoring and data collection opportunities to capitalise on, but only if they prove to be methodologically robust and fulfil the evaluation criteria for a national natural capital asset.

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Table A3. Potential indicators for assessing the natural capita of soils for enclosed farming (cropland) Indicator

Service

Frequency

Soil health indicator: pH (5.9-6.4), soil organic carbon, compaction, aggregate stability

Flood attenuation/Water Quality, Provisioning -crops

25 years

Data sources JHI- NSIS

Soil compliance monitoring; pH, N, P, K, Carbon, toxic elements, microbial carbon biomass, earthworms GAEC Soil Protection Reviews: – soil organic matter, erosion risk

Flood attenuation/Water Quality, Provisioning -crops

Every year

SEPA

Flood attenuation/Water Quality, Provisioning -crops

Every year

Selfreported by farmers

Flood attenuation/Water Quality, Provisioning -crops

8-10 years

CS

Soil carbon

Notes

Traffic light

The National Soil Inventory of Scotland is based on two data monitoring points, 1978-1987 and 20072010. Although comprehensive it does not provide the monitoring frequency required for an annual index Limited sampling in Scotland (e.g. 22 farms in 2010) and a bias to sites which apply waste; sewage sludge, distillery waste (potential contamination) Farmers have to complete an annual Soil Protection Review as part of cross compliance for single farm payment. Unsure how self-reported monitoring can be utilised as a data source? Data collected every 810 years

References Britt, C. and Johnston, M. 2008. Trees in Towns II: A new survey of urban trees in England and their condition and management. ADAS UK Ltd. and Myerscough College. A report for the Department of Communities and Local Government, London. Dobbie, K.E., Bruneau, P.M.C and Towers, W. 2011. The State of Scotland’s Soil. Natural Scotland. www.sepa.org.uk/land/land_publications.aspx. Edwards, D., Elliott, A., Hislop, M., Martin, S., Morris, J., O’Brien, L., Peace, A., Sarajevs, V., Serrand, M. and G. Valatin. 2009. A valuation of the economic and social contribution of forestry for people in Scotland. Forestry Commission Research Report. Forestry Commission Scotland, Edinburgh. Haines-Young, R. and Potschin, M. 2010. The links between biodiversity, ecosystem services and human well-being. Chapter 6, pp. 110-139. In: Raffaelli, D.G. and Frid, C.L.J. (eds). Ecosystem Ecology: A New Synthesis. Cambridge University Press, Cambridge.

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Natural History Museum. 2013. Urban Tree Survey online/british-natural-history/urban-tree-survey/index.html.

http://www.nhm.ac.uk/nature-

Scottish Government. 2011. Getting the best from our land: A land use strategy for Scotland. http://www.scotland.gov.uk/Publications/2011/03/17091927/0. Willis, K.G., Garrod, G., Scarpa, R., Powe, N., Lovett, A., Bateman, I.J., Hanley, N., and Macmillan, D.C. 2003. The social and environmental benefits of forests in Great Britain. Social Benefits of Forestry: Phase 2. Report to Forestry Commission. Centre for Research and Rural Appraisal and Management, University of Newcastle.

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