experimental species accounts for the eu · 2019. 3. 21. · type in the first period, and...

Experimental Species

Accounts for the EU

The report has been produced by UNEP-WCMC in collaboration with the European Environment Agency as part

of the integrated system for natural capital and ecosystem services accounting (KIP INCA) project. The report

provides specific outputs set out for Tasks 2.2, 2.3 and 2.4, under a technical support contract to KIP-INCA

funded and managed by the Directorate-General for Environment of the European Commission.

UNEP-WCMC Technical report

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Authors

Steven King, Mike Harfoot, Katherine Moul, Arnout van Soesbergen & Claire Brown (UN Environment World

Conservation Monitoring Centre)

In collaboration with: European Environment Agency

Acknowledgements

We are very grateful to Petr Vorisek at the European Bird Census Council for providing advice on the project

and facilitating discussions with national coordinators of bird monitoring schemes in Europe. We thank the

Czech Society of Ornithology for provision of the Breeding Bird Census Programme (Jednotný Program Sčítání

Ptáků, JPSP) data, in particular Zdeněk Vermouzek and, Jiří Reif. We are also very grateful to the British Trust

for Ornithology for provision and processing of the UK Breeding Bird Survey (BBS) data, in particular to Gavin

Siriwardena and David Noble and the advice they have provided for the project. The BBS is funded by a

partnership of the British Trust for Ornithology (BTO), Joint Nature Conservation Committee (JNCC) and Royal

Society for the Protection of Birds (RSPB).

Published

March 2019

Front Cover Photo

© Allan Hopkins 2016 CC BY-NC-ND 2.0 courtesy of Flickr

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Suggested Citation

UNEP-WCMC (2019) Exploring Experimental Biodiversity Accounts for the EU. UNEP-WCMC. Cambridge,

UK.

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Contents Executive Summary ................................................................................................................................................ 2

1. Introduction ......................................................................................................................................................... 5

2. Bird ‘Species Accounts’ ...................................................................................................................................... 6

2.1. Testing a Concrete Spatial Approach to Species Accounting ...................................................................... 6

2.1.1. Overview of the Measurement Approach ............................................................................................. 6

2.1.2. Bringing the data together ..................................................................................................................... 7

2.1.3. Czech Republic Test Case ..................................................................................................................... 9

2.1.5. United Kingdom Test Case ................................................................................................................. 12

2.2. Testing Methodological Assumptions ........................................................................................................ 16

2.3. Comparative analysis with Article 12 based accounts ............................................................................... 19

2.3.1. Czech Republic Comparative Analysis ............................................................................................... 19

2.3.2. United Kingdom Comparative Analysis ............................................................................................. 20

2.4. Conclusions ................................................................................................................................................ 21

3. Experimental Integrated Biodiversity Account ................................................................................................. 24

3.1. Organising spatial data on biodiversity using the SEEA EEA ................................................................... 24

3.2. Testing a Minimum Spatial Approach ....................................................................................................... 24

3.3. Developing a Fully Spatial Approach ........................................................................................................ 28

3.3.1. Modelling test using BTO data aligned with the 1km Accounting Grid ............................................. 28

3.3.2. Options for moving to EU scale full spatial approach (Roadmap) ...................................................... 31

3.3.3. Other Spatial Datasets that can be integrated into the Roadmap ......................................................... 34

3.3.4. Next Steps for the Roadmap ................................................................................................................ 36

3.4. Considering ecological risks in the context of SEEA ................................................................................ 37

3.4.1. Risk Register Approach ...................................................................................................................... 37

3.4.2. Test Case Risk Register ...................................................................................................................... 39

3.4.3. Policy Insights from the Test Case Risk Register ............................................................................... 41

3.4.4. Options for Development of the Test Case Risk Register ................................................................... 42

3.5. Conclusions ................................................................................................................................................ 43

References ............................................................................................................................................................. 44

Appendix B: Pollinator data sources (Task 2.3 by Institute for European Environmental Policy: IEEP) .. Error!

Bookmark not defined.

Appendix C: Overview of species data sources ................................................. Error! Bookmark not defined.

Executive Summary This report summarises a set of technical support actions for compiling biodiversity-related natural capital

accounts using the SEEA-EEA framework and work advanced under the EU MAES initiative. The compilation

of spatially referenced common bird Species Accounts using survey data from the Czech Republic and United


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Kingdom are tested. Common birds are selected, as they are good indicators of ecosystem condition. The test

cases provide statistics on species richness, Shannon index and abundance, aligned to Corine Land Cover (CLC)

editions (2000, 2006 and 2012). This allows information to be presented by MAES ecosystem type. The tests

demonstrate that it is possible to construct geo-referenced common bird Species Accounts by MAES ecosystem

type. However, data gaps are identified for Urban (UK) and rare ecosystem types (e.g., heathland / shrub).

The trends in bird species statistics presented in the accounts, are found to be somewhat inconsistent between the

2000 to 2006 and 2006 and 2012 accounting periods (i.e., trends maybe upwards in a given MAES ecosystem

type in the first period, and downwards in the second). This is especially for abundance estimates, which are

particularly volatile for the Czech Republic test case. Generally, the results of the UK test case are more closely

aligned with the national trends in common bird indicators. This likely reflects the more comprehensive nature of

the UK dataset. Some methodological refinement of the accounting approach is still required. For example,

focusing on specific subsets of common birds in different Member States or geographies; identifying subsets of

common birds to inform on condition in different MAES ecosystem types, calculating species diversity measures

using survey site averages; and, using relative abundance indexes in an accounting context should all be explored

further.

A comparative analysis between the spatially referenced Common Bird Species Accounts and similar accounts

compiled using data reported by Member States under Article 12 of the Birds Directive. The Article 12 based

approach is based on disaggregating data reported by Member States, on the basis of expert judgement of their

MAES ecosystem type preferences (the approach is described in UNEP-WCMC, 2017). For the UK, this

suggests that the disaggregation procedure inherent in the Article 12 based approach is too narrow for many

common bird species, as it assumes that they make use of maximum of three MAES ecosystem types. However,

a number of species may be more generalist in their ecosystem use than this. For the Czech Republic, national

monitoring data is found to be available for far more bird species than reported under Article 12 of the Birds

Directive. It is understood that this reflects that the Czech Republic only report on Annex I bird species in their

2012 reporting, and a comprehensive set of Article 12 species will be reported on for the 2013 to 2018 reporting

period.

This report also presents an experimental Integrated Species Account, which integrates data on a range of species

with ecosystem extent data (both key components of biodiversity). This reveals that the trends in individual

species-level biodiversity metrics are generally consistent by MAES ecosystem type, at the EU level. This

provides some convergent validity with respect to the trends in bird species statistics identified by MAES

ecosystem type using the Article 12 based approach at the EU scale. However, only limited species data could be

found for integration into the account, beyond statistics on bird species. Further work should be prioritised to

collate and process species data, to increase the taxonomic coverage of ecosystem accounts in the EU. However,

fundamentally, data on taxa other than birds (and butterflies in some countries), do not exist because there are no

established survey schemes. As such investment in addressing these the monitoring gaps is critical, if wider

taxonomic coverage for geo-references species data is to be achieved in the EU.

Exploratory modelling is presented in order to potentially achieve an EU scale spatial extrapolation of bird

species data. Initial results are encouraging, based on the UK test case and Corine Land Cover data. However,

the harmonisation of data across national monitoring programmes represents a significant challenge, and reflects

the need for more rigorous assessment of methodologies when dealing with the diverging sampling strategies

employed. Therefore, obtaining an ecologically meaningful compilation of bird based Species Accounts for the

EU on a regular and basis, will require a significant joint effort with ornithologists.

The best possibility of expanding taxonomic coverage is found to be associated with butterfly monitoring

schemes, although less well developed than bird monitoring schemes. Again a significant joint effort with

butterfly experts will be required, if these data are to be harmonised at an EU scale.

A key objective of ecosystem accounting is to combine information in a consistent framework, using ecosystem

assets as the conceptual unit for such integration. The use of risk registers has been proposed as a means of

identifying ecosystem assets, where further loss of condition places ecosystem service delivery at risk. This

report shows that risk registers can provide a qualitative overview of these links between different ecosystem


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accounts, including the integrated biodiversity account presented in this report. The use of combined

presentations drawing on the risk register are shown to provide useful context to decision-makers and planners.


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1. Introduction To build the knowledge base for better management of natural capital in the EU, a shared project was set up to

develop an integrated system for natural capital, and ecosystem services accounting (KIP INCA). The

methodological starting point of KIP INCA is the UN System of Environmental-Economic Accounting-

Experimental Ecosystem Accounts (SEEA EEA) (UN et al., 2014). KIP INCA also connects to the first phase of

the EU initiative on Mapping and Assessment of Ecosystems and Services (MAES).

The SEEA EEA defines are ‘Ecosystem Assets’ as the conceptual accounting unit, represented by delineating

homogenous areas of different ecosystem types such as grassland, woodland and forest, lakes and rivers,

cropland or wetlands (UN et al., 2018). The delineation of Ecosystem Assets also establishes an infrastructure

for organising information on a wider range of ecosystem accounting themes. This includes information on

biodiversity, as well as wider ecosystem condition parameters, ecosystem services supply and beneficiaries, all

organised via a set of integrated ecosystem accounting tables.

At an early stage of the SEEA EEA implementation, consideration is required on the measurement pathway to

compilation of the different ecosystem accounts and the spatial scale and flexibility with which the underlying

data is organised. In this regard, the SEEA EEA Technical Recommendations (TR) (UN et al., 2018) sets out a

spectrum of minimum to fully spatial approaches.

At one end a “minimum spatial approach” would provide a broad overview of ecosystem assets, biodiversity and

wider thematic and ecosystem service concerns by ecosystem type. At the other end of the spectrum, a “fully

spatial approach” would be built using detailed geospatial data. This will be created at sufficient spatial

resolution to capture the key ecosystem features of interest (such as species diversity statistics as indicators of

ecosystem condition), which are integrated via a coordinated spatial data system. This could be based on

delineation of Ecosystem Assets as polygons in a GIS based system but, more typically, such a system is

underpinned by grid based spatial referencing system. For the EU it is proposed to adopt a 1km core accounting

grid, as discussed herein.

The minimum spatial approach is suited to data with limited spatial resolution, such as those available from the

EU Nature Directives Reporting. The fully spatial approach represents the ambition for a flexible ecosystem

accounting approach for the EU, where ecosystem data is organised within a 1km core accounting grid. Whilst

this will never be feasible using solely primary monitoring data, it could be done in principle using modelled

abundance or presence surfaces for different taxa.

The prime motivation for ecosystem accounting is that separate analysis of ecosystems and the economy does

not adequately reflect the fundamental relationship between humans and the environment. As such, the full

added value of organising environmental information using the SEEA EEA lies in more facilitating more

detailed spatial analysis, linking to existing information on economic sectors and wider beneficiaries of

ecosystem services. This provides a more complete picture of natural capital (and related ecosystem services), as

complement to economic measures of a nation’s wealth. This ‘fully spatial’ approach also requires geospatial

data at sufficient spatial resolution to capture the key ecosystem features of interest in a harmonised and fully

compatible format. Consequently, good spatial referencing can substantially improve the potential of data to

support ecosystem accounting. It is also an essential prerequisite for organising data to accurately correspond

with the spatial scale and location of ecosystems. As the SEEA EEA TR acknowledges, the range of policy

applications are greater when a fully spatial approach is implemented.

In order to support KIP INCA, this report explores the potential of key European biodiversity datasets to inform

a fully spatial ecosystem accounting approach for the EU. Section 2 explores options to compile spatially

referenced ecosystem accounts using national bird monitoring data (termed Species Accounts), and how these

could be used in the context of assessing ecosystem condition. Section 2 also compares these results against the

signals that emerge from aggregate approached based on national reporting of data under Article 12 of the Birds

Directive. Section 3 reviews possibilities for more integrated accounting for biodiversity, using wider taxonomic

data. This starts from a minimum spatial approach and then onto exploring possibilities for achieving the fully


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spatial approach grounded in the core 1km accounting grid that is the ambition for ecosystem accounting in the

EU. Finally, Section 3 presents an application of risk registers to identifying ecosystem assets where further loss

of condition, based on trends in species-level biodiversity metrics, places ecosystem service delivery at risk.

2. Bird ‘Species Accounts’ The objective of this task (Task 2.2) is to develop the Species Accounting approaches presented in UNEP-

WCMC (2017), which were based on using data on birds reported under Article 12 of the Birds Directive. There

were several important next steps identified for this work that could improve the accounts in terms of their

thematic, temporal and spatial resolution. This task will focus on two of these, as identified in the conclusions of

the “Testing the development of species accounts for measuring ecosystem condition at EU level” report

(UNEP-WCMC, 2017). Specifically:

● Using national monitoring data (including data collected to inform the PECBMS) to inform a more

concrete spatial ecosystem accounting approach.

● Undertake comparative analysis between the trends reported for common bird species via national data,

and the data generated by the Article 12 Species Accounts as a form of validation.

2.1. Testing a Concrete Spatial Approach to Species Accounting The first activity under this task was to support the European Environment Agency (EEA) in the framework of

KIP INCA, to explore opportunities to test the use of other datasets that are collected in a spatially referenced

manner. The datasets collected by European Bird Census Council (EBCC) partners are identified as a prime

example of such. In particular, the spatially referenced data on common bird species that underpins the reporting

of trends under the Pan-European Common Bird Monitoring Scheme (PECBMS). This dataset is considered

most relevant, and currently covers a total of 168 species in 28 countries (EBCC, 2018). Common birds are

sensitive to environmental changes, and their abundance is acknowledged as a good indicator of environmental

health. Indeed, the derived common bird and common farmland bird indices are adopted by Eurostat as

indicators for sustainable development (Eurostat, 2018). Consequently, spatial and temporal trends in the status

of common birds, can be used to infer the condition of ecosystems in the EU.

Engagements with the EBCC were undertaken to establish suitable test case countries for testing an approach to

Species Accounting, using spatial data on species covered by the PECBMS. Suitable countries with time-series

of georeferenced bird data that could be aligned with Corine Land Cover editions were determined. In order to

test the accounting approach, data needed to be collected with a sound methodology, and readily accessible with

limited processing required. These discussions identified the British Trust for Ornithology (BTO), Joint Nature

Conservation Committee (JNCC) and Royal Society for the Protection of Birds (RSPB) led Breeding Bird

Survey (BBS) and the Breeding Bird Census Programme (Jednotný Program Sčítání Ptáků, JPSP) coordinated

by the Czech Society for Ornithology (CSO) as suitable datasets.

2.1.1. Overview of the Measurement Approach

The measurement approach to calculating Species Accounts using the BBS and JPSP data is relatively simple.

The idea was to use georeferenced bird species data (presence and absolute abundance) from the BBS and JPSP

(black crosses in Figure 1). This data needed then to be associate with the respective MAES ecosystem type at

the location, using the Corine Land Cover product for that year, either at 1km or 1ha resolution (the squares in

Figure 1).1 Bird data could then be aggregated by MAES ecosystem type (identified by the colour of the squares

in Figure 1), to whichever scale desired. Spatially referenced bird based Species Accounts could then be

compiled for these areas. One potential caveat to this approach, discussed further in the discussion of

methodological limitations (Section 2.4), is that land cover that is commonly found in small patches, such as

1 The MAES typology comprises of nine terrestrial and three marine ecosystem types. The nine terrestrial ecosystems types

are derived from aggregations of the 44 Corine Land Cover Level 3 classes. They comprise: Cropland, Grassland, Heathland

/ shrub, Sparsely vegetated land, Marine inlets, Rivers and lakes, Wetland, Urban, Woodland / Forest. The crosswalk between

CLC and MAES typologies and further details are provided in MAES (2013)


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small urban areas or areas of sparse vegetation will not be well represented at the 1km2 scale. The national scale

was chosen for testing as this allowed a comparative analysis with Species Accounts generated, using the Top-

Down Article 12 based approach developed in 2017 (as shown in Figure 1).

Figure 1: Bottom up approach to compile Species Accounts

2.1.2. Bringing the data together

The BBS data aims to provide a consistent, repeated sampling of 1km squares over years and increased coverage

of under-sampled habitats. However, the JPSP sampling strategy is based on free choice, consequently there is

much less repeated sampling of the same locations. In addition sampling effort is observed to increase

substantially over time. This is a common type of non-harmonised data situation. One way to achieve a

consistent measurement approach, would be to focus on the consistent sampling locations between periods only.

Figure 2 presents this in the diagram form, where consistent sampling locations are represented by black crosses.

Figure 2: Issue of inconsistent and increasing sampling


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However, this is clearly undesirable as a lot of information is discarded, and there would be no incentive to

invest in improving biodiversity monitoring programmes with such a treatment. It would also be expected that

over time this set of consistently monitored locations would tend to zero, and ecosystems will be converted to

different land uses at some locations. In order to overcome these issues, the principal approach to compiling

Species Accounts using spatially referenced bird survey data is based on two key assumptions listed below:

● Identifies all common bird species in their preferred ecosystems. This is considered reasonable, given

they are common birds and the MAES ecosystem typology is broad – particularly for large scale

accounting applications.

● Provides counts of common bird species that are representative of the real distribution of common bird

populations (i.e., their abundances relative to each other in different MAES ecosystem types).

The second assumption deserves closer examination, as in reality observed abundances can be unrepresentative

of the true distribution of common bird species. This is because some species are harder to detect than others,

and their detectability can also vary as a function of both their abundance, and the habitat being observed. This is

acknowledged as a source of potential bias in the accounting approach, and it is recommended below (Section

2.4) that the complex issue of detectability is included in future methodologies.

If these assumptions are accepted, then bird species richness and evenness statistics can be calculated using all

data from different monitoring visits, and compared for the same ecosystem accounting area (by ecosystem type)

or asset. This allows comparisons to be made over time and between ecosystem types.

Species richness is derived for each ecosystem type, by calculating the number of unique common bird species

observed across all survey sites within each ecosystem type, for a given period. There are a number of different

diversity indices that measure population evenness. For the Species Accounts using bird survey data, the

Shannon Index is adopted. This follows the approach in UNEP-WCMC (2017). This takes both the number of

species and their abundances into account, and provides low values when a few species dominate and high

values when no single species dominates (Van Strien, Soldaat and Gregory, 2012).

Abundance based measures can be expected to vary in-line with the intensity of sampling carried out. To enable

comparison between years with different sampling intensities (i.e., where both the number of survey sites and

their locations vary), the abundances of each common bird species is calculated for each ecosystem type relative

to the area sampled (e.g., No. birds / km2 of cropland). The Shannon Index can then be calculated directly from

these relative abundance measures. This reflects that the result is entirely equivalent to that calculated when

using the total absolute abundance per ecosystem type.

As the Shannon Index combines information on species richness and evenness, it potentially conveys more

information on biodiversity than a dominance indicator, such as the Simpsons Index. This has utility in a wider

accounting context, as it integrates information on bird species richness and evenness in a single condition

parameter. However, there is no reason why the accounts could not be readily updated to include different

diversity indicators.

Species Accounts organise information on the stocks of species. Species richness can be considered a measure of

this, but measures of abundance are of fundamental concern to environmental asset accounts (e.g., standing

stocks in forest accounts, water volumes in water accounts, etc.). A simple approach to account for bird species

stocks is, to scale-up the relative abundance measures described above, by the area of a given ecosystem type in

any given ecosystem accounting area (e.g., No. birds / km2 of cropland multiplied by area of cropland).

The X’ cells in Figure 3 illustrate this type of approach to inferring abundance in non-surveyed grid cells based

on the surveyed counts from the X cells. As Figure 3 suggests, in theory, this could be done at the scale of each

individual ecosystem asset, if a sufficiently comprehensive sampling strategy was provided. However, in most

cases this is an unreasonable expectation. Again, caution needs to be taken on how such results are

communicated and employed at different scales, as spatial heterogeneity in species abundance within ecosystem

types is clearly to be expected. This is especially true for the MAES ecosystem types, given their broad

ecological range.


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Figure 3: Extrapolating species abundance measures

As discussed in Section 2.4, detection probability would influence this spatial extrapolation, and should be

considered in future analysis. A more sophisticated approach could use modelling to impute missing abundance

values in cells that are not surveyed. An analysis called TRends and Indicators for Monitoring data (Pannekoek

and Van Strien, 2006) has been used for imputation with monitoring data. However, this requires a survey to

have been carried out at a site in the past. Extrapolation based on relationships between abundance and spatial

covariates can be used, whilst also taking into account issues such as detectability. This is discussed further in

Section 2.4.

2.1.3. Czech Republic Test Case

The Breeding Bird Census Programme (JPSP) for the Czech Republic has been ongoing since 1982. The JPSP

survey design is based on Point Count Transects (PCT). Under the PCT method, the volunteer walks a 6 km

transect and conducts bird counts at 20 points, each being 300m apart, along the length of the transect. The

volunteer is free to choose where they complete the transect. A counting session at each point lasts 5 minutes.

Two walks of the transect are recommended to volunteers but this is not obligatory. The transects are scattered

throughout the country and cover all main habitats. Most transects consist of a mixture of habitats. As data have

come from all points in all habitats, it is reasonable to assume they are representative of the Czech Landscape

(Reif et al., 2007). Figure 4 shows the location of the transects in the Czech Republic for the year 2012, overlaid

on the Corine Land Cover Map of the same year.

Figure 4: Transect locations for the Czech Republic JPSP, overlaid with CLC for 2012


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2.1.3.1. Detailed methodology for the Czech Republic Test Case

In order to inform a comparative analysis, all the transect data for the common bird species considered in the

Article 12 based accounts presented in UNEP-WCMC (2017) were extracted from the JPSP dataset. These are

the 167 common bird species listed under the Pan-European Common Bird Monitoring Scheme (2017)

(PECBMS). For some transects, geo-referencing of the individual point locations was not available. Instead,

these were mapped to the nearest settlement. Given this spatial uncertainty, these transects were dropped from

the dataset. For the remaining transect, the mean average counts of species at each point within a year was

calculated.

In order to allow alignment with the CLC maps, the JPSP data was organised in subsets relevant to the 2000,

2006 and 2012 editions. To account for inter-annual variation in population abundances, for each edition (2000,

2006 and 2012), JPSP data was considered from the previous year, the edition year and the following year. For

example, for the 2000 CLC edition, JPSP data from the 1999, 2000 and 2001 surveys were included. This also

allowed for a larger dataset to be generated for alignment with each CLC edition. For each CLC edition, the

maximum abundance for each species within each transect point over the three years was then selected as the

abundance measure. Species richness was then calculated as the total species richness over the three year period

observed at any given transect point.

For the spatial referencing of the accounts, it is assumed that the count results at any point on the transect

represent a count of birds within a hectare, with the point representing the centroid of a square hectare. For each

transect point (or associated hectare), a CLC type was determined via GIS techniques by overlaying the

georeferenced coordinates for the point with the CLC accounting layer. This allowed for a cross-walk to the

MAES Ecosystem type to be performed, and a MAES type attributed to the hectare where the count was

performed.

In order to compile the Species Accounts, species richness was then derived for each MAES ecosystem type by

calculating the number of unique common bird species observed across all counts within each ecosystem type,

for the relevant CLC edition (i.e., 2000, 2006 and 2012). Total abundance of common birds for each MAES type

was then obtained by summing the observed absolute abundances of each species in each point location, within

each ecosystem type. Using these two pieces of information, a Shannon index of diversity was then calculated

for each MAES ecosystem type. As introduced earlier, the detectability of each species should be incorporated

into the analysis, to avoid the potential bias introduced by using the raw observed abundances.

In order to estimate the abundance of common bird species by ecosystem type at the Czech Republic scale, a

population density / relative abundance (in common birds per ha) was calculated. This equals the total abundance

for each ecosystem type observed, divided by the total area of each ecosystem type sampled by the JPSP during

the analysis years. This was then multiplied by the total extent of that ecosystem type (in ha) within the Czech

Republic, to derive an estimate of the total stocks (or abundance) of common bird species.

2.1.3.2. Czech Republic Spatially Referenced Common Bird Species Accounts

A review of the derived JPSP dataset revealed that the number of transects was increasing significantly through

time (50 for 2000, 121 for 2006 and 143 for 2012). Tables 2 and 3 present the Common Bird Species Accounts

using all the JPSP data. In addition, given the steep increase in sampling intensity over time, Common Bird

Species Accounts have been compiled using consistently sampled locations for 2000, 2006 and 2012. These are

presented in Appendix A.

Table 2 identifies a substantial increase in species richness between 2000 and 2006 in every ecosystem (up to 21

species in Grasslands). This could be an artefact of variation in sampling effort (evaluated further in Section 2.2).

Generally, the Shannon index is increasing across all ecosystems, with the exception of cropland (-0.05). Table 2

shows very large increases in common bird abundance between 2000 and 2006 (by around 65 million). Again,

this is driven by increases in Cropland (by around 35 million).


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Table 2: Czech Republic Common Bird Species Account (2000 - 2006)

Table 3 reveals that the Shannon index decreases across all MAES ecosystem types between 2006 and 2012. The

maximum decrease is associated with Grassland (-0.38). Reductions in species richness are also observed in

Wetland (-4) and Woodland / Forest (-1), although species richness increases in all other ecosystems and is

positive for the Czech Republic as a whole (+4). The trends in species richness in Cropland, Grassland and

Urban are consistent between Table 3 and the accounts based on consistently sampled locations (see Table A2,

Appendix A), but are not for Rivers and lakes and Urban ecosystems.

Table 3 also shows a substantial reduction in the estimated abundance of birds (around 18 million across all

ecosystems), which is consistent with the accounts based on consistently monitored locations (Table A2). This

is, again, driven by reductions in cropland (around 22 million).

It is noted that the abundance estimates presented in Tables 2 and 3 are very high (approaching ⅓ of a billion).

The estimated abundance of all bird species in the Europe (at the Pan-European scale, larger than the EU) is 4

billion breeding pairs (BirdLife, no date). Given the relative size of the Czech Republic to the Pan-European

landscape, the results on abundance estimates in Table 2 and 3 are considered spurious.

The 2014 Report on the Environment of the Czech Republic (CENIA, 2015), provides a set of bird indicators

that can also be used to sense check the results presented in Tables 2 and 3. The bird indicators in this report are

also derived from the JPSP data and calculated by the CSO, thus providing a solid benchmark against which to

compare the accounts. This report identifies a decreasing trend in common bird populations. The common bird

indicator decreases by 7.6% between 1982 and 2014, at an even rate. This suggests the picture of increasing

population as provided in Table 2 between 2000 and 2006 could be spurious, although this trend in the common

bird index is well represented by the aggregated results presented in Table 3.

Table 3: Czech Republic Common Bird Species Account (2006 - 2012)


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CENIA (2015) also indicates that farmland birds show larger decreases, with the population numbers dropping

by 27.5%, essentially between 2000 and 2014. Again this supports the results presented in Table 3 for cropland

and grassland but not for Table 2. Woodland birds also demonstrate a steady decline (18.9% between 1982 and

2014). This clearly does not reflect the results presented for woodland / forest ecosystems in Tables 2 and 3.

However, this could be due to considering a wider set of common bird species in Tables 2 and 3, rather than

those more specialised farmland or woodland groups of birds.

2.1.5. United Kingdom Test Case

The BBS was launched as an annual monitoring programme in 1994. The survey design of the BBS is based on

monitoring a set of randomly selected 1 km squares in the National Grid. A stratified approach is used to select

the squares for surveying, based on splitting the UK into its different regions and then randomly selecting

squares within each region based on the number of potential BBS volunteers available for each region. One of

the stated aims of the survey is for as many of the same 1 km squares to be surveyed every year, allowing a time

series of comparable results to be generated. Nonetheless, the overall number of squares samples each year is

generally increasing the described random manner. For example, in 2000 at total of 2,248 squares were

surveyed, in 2006 it was 3,295 and in 2012 it was 3,430 (BTO, no date). Figure 5 presents the distribution of

BBS squares for the UK in 2012.

In any given year, the same volunteer visits the survey square two times to record bird observations, one in the

early part of the breeding season (April to mid-May) and one at least four weeks later (mid-May to end of June).

The survey is based on completing two 1 km transects (routes) across the survey square. As far as possible, the

transects should be at least 500m apart from each other and 250m from the edge of the square. Each transect is

divided into 200m sections and the volunteers record all the birds that they see or hear as they methodologically

walk each section. The total number of each bird species (i.e., summed over all transect sections) is then

calculated. The BBS approach is to take the maximum of the two counts for each species as the relative measure

of abundance of that species in the 1 km square. This measure is considered relative, because the assumption is

that an observer records a consistent proportion of a species abundance through time, so these species

abundances are comparable. However, the absolute abundance of each species remain biased by the variation in

detectability.


13

Figure 5: Distribution of BBS squares, 2012

In order to inform a comparative analysis, the BTO extracted data on all the common bird species considered in

the Article 12 based accounts presented in UNEP-WCMC (2017).The data abstracted for each of these species

comprised the Ordnance Survey grid reference for the SW corner of the 1km grid, the year the square was

surveyed, the BTO species code, the species name and EURING code2 for birds counted and the maximum count

of that species per square and year. It is important to note that the extracted data excluded birds in flight – this

avoids including data on birds that are flying over the square and not actually using it for habitat.

2.1.5.1. Detailed methodology for the UK Test Case

The BBS data uses the UK Ordnance Survey and Irish Ordnance Survey map reference systems.3 The CLC data

is available at a resolution of 100 metres in the EEA reference system. To align these datasets, the common 1-km

resolution UK EEA grid shape file was used (EEA, 2017). For each 1km BBS grid cell, centroids were

calculated and joined spatially with the EEA grid shape file thus creating unique combinations of EEA grid

codes and species survey data. The area of each land cover class for the CLC layers (2000, 2006 and 2012)

within EEA grid cells was then extracted. The BBS data was then joined with the CLC data using the unique

EEA grid codes. Each 1km grid cell was assigned a MAES type, this was based on assigning the CLC level 3

class to their parent MAES type at the 100m resolution and then determining the MAES type that occupied the

largest proportion of the 1km grid cell area. This is analogous to the approach to derive Ecosystem Extent

Accounts for Europe, currently undertaken by the EEA.

2 A standard set of codes bird ringing schemes in Europe 3 It should be noted that the Channel Islands are also included in the BBS data but have not been included in the analysis.

This results in omission of 1 to a maximum of 2 species from the analysis.


14

Following the Czech Republic approach, data from year of CLC evaluation, the preceding and subsequent years

was used. So for 2006, BBS data from 2005, 2006 and 2007 was analysed. The year 2001 was anomalous for the

BBS because the foot and mouth disease outbreak prevented access to large areas of the British countryside.

Hence the survey data was very limited compared to other years. Therefore for the CLC year 2000, BBS data

from the years 1999, 2000 and 2002 were assessed. For these analysis years, the maximum abundance for each

species of common bird was taken within each CLC grid cell, where BBS sampling took place over each three or

two year period. Therefore, the total abundance for grid cell was the maximum of these species level abundances

observed. The species richness of the grid cell was calculated as the number of common bird species observed in

the grid cell, over the 3 years of BBS data.

Following the approach detailed for the Czech Republic, species richness was calculated by summing the

number of unique species observed across all grid cells within each MAES ecosystem type. Total abundance of

common birds observed in each MAES type was obtained by summing observed abundances of each grid cell,

within each ecosystem type. A Shannon index of diversity was calculated using the bird abundance data

aggregated to ecosystem type.4 This allowed a comparison with the Article 12 approach presented in UNEP-

WCMC (2017).

As with the Czech Republic test case, a population density / relative abundance measure (common birds per

km2) was also calculated. This was then multiplied by the total extent of that ecosystem type (in km2) within the

UK to derive a lower bound estimate of the total stocks (or abundance) of common bird species. This is a lower

bound as birds in flight are part of the UK stock, but not included in the account as they are not situated within a

terrestrial ecosystem at the point of observation. As such it is not meaningful to consider them as an indicator for

the condition of that ecosystem. However, this raises an interesting question on how to treat accounting for

mobile species in a terrestrial environmental accounting context. As there will be a contrast between species

almost always recorded in flight and those recorded both in flight and in habitats, the former are likely to be

under-represented in Species Accounts. However, this may not be a significant issue when the purpose is to

inform on ecosystem condition only but this requires further evaluation.

2.1.5.2. UK Spatially Referenced Common Bird Species Accounts

The Common Bird Species Account for the period 2000 to 2006 is presented in Table 4. Generally, the account

reveals that the overall stocks (abundances) of common birds are decreasing between 2000 and 2006. In fact, the

only increase is observed for Rivers / Lakes (+202,457 birds). The largest decrease in abundance is associated

with Grassland ecosystems (-1,981,608). Conversely, species richness is increasing overall and in all ecosystems

except Marine Inlets. However, the Shannon index suggests that evenness in the abundance of species is

reducing most in Rivers / Lakes (-0.21), despite overall abundance and species richness increasing. These

situations where populations become less diverse may be indicative of a loss of condition.5 A very marginal

decrease in the Shannon’s Index is noted for Cropland (-0.03) and Woodland / forest (-0.01) ecosystems. The

highest increase in the Shannon’s Index is observed in wetlands (+0.27). A key observation for Table 4 that no

BBS surveys appear to have been made in 1km cells classified as urban ecosystems.

Table 4: UK Common Bird Species Account (2000 - 2006)

4 It is acknowledged that using the average Shannon Index value all survey sites within a given ecosystem type may be a

more appropriate indicator of the condition of that ecosystem. This is discussed in Section 2.1.5.2 5 It is noted that a loss of population evenness may also occur when a sensitive species re-establishing itself with a low

abundance in an area. So this metric may need to be interpreted in certain instances.


15

Table 5 provides the Common Bird Species Account for the period 2006 to 2012. Again, an overall increase in

the total stocks of common birds is observed. However, abundances are declining in Marine Inlets (-35,143),

river / Lakes (-184,995) and sparsely vegetated (-14,126) ecosystems. Albeit, these reductions have, typically,

been preceded by similar additions in the 2000 to 2006 accounts indicating volatility in abundance estimates. A

reduction in species richness is observed in Grassland (-2), Heathland / shrub (-5), Rivers / Lakes (-7) and

wetlands (-4). Counterintuitively, this is generally associated with an increase the Shannon index for that

ecosystem. The exception is wetland, where the Shannon index decreases (-0.13).

Overall, there are few consistent trends over the two accounting periods. These are limited to increases in

population abundance and species richness in croplands and woodland / forest and increases in population

abundance in grasslands and wetlands. In order to sense check these results, reference has been made to the UK

official statistics on wild bird populations. The most recent identify that wild bird populations (based on an index

of 130 species) report no significant changes between 2011 and 2016, with the current value of the index

appearing relatively similar to that in 1990 (DEFRA, 2018). This would tend to support the experimental

accounting results presented. The woodland bird index also appears relatively stable between (approx.) 1995 and

2010. However, the farmland bird index demonstrates a relatively consistent decline from 1990. The failure of

the Common Bird Species Accounts to reflect this trend could be indicative of a loss of sensitivity in the

accounts due to focusing on too wide a set of species, rather than subsets with particular, more specialist habitat

affiliations. As such the differentiation between generalist and specialist species is very relevant for

interpretation of the Species Accounts and should be considered in further development of this work.

Table 5: UK Common Bird Species Account (2006 - 2012)

Cropland Grassland

Heathland /

Shrub Marine Inlets Rivers / Lakes

Sparsely

Vegetated Urban Wetlands

Woodland /

Forest All Ecosystems

Total abundance

(No. individuals)18,212,483 22,586,123 2,444,857 436,113 632,729 496,407 - 2,003,963 5,386,900 52,199,576

Number of

species96 95 80 68 70 60 - 70 88 100

Shannon's Index3.24 3.36 2.96 3.00 3.49 3.13 - 2.57 3.32 3.38

Total abundance

(No. individuals)-486,453 -1,981,608 -350,519 -28,171 202,457 -74,840 - -114,394 -203,388 -3,036,916

Number of

species1 3 5 -1 3 7 - 11 2 5

Shannon's Index-0.03 0.03 0.17 0.07 -0.21 0.10 - 0.27 -0.01 0.01

Total abundance

(No. individuals)17,726,030 20,604,516 2,094,338 407,942 835,186 421,568 - 1,889,569 5,183,512 49,162,660

Number of

species97 98 85 67 73 67 - 81 90 105

Shannon's Index3.21 3.39 3.13 3.07 3.27 3.22 - 2.84 3.32 3.39

MAES

Open (2000)

Net Change

Close (2006)


16

2.1.5.2. UK Shannon Index by Grid Cell

In the analysis provided in Tables 4 and 5, Shannon Indices are calculated using the bid data aggregated by

ecosystem type. This allows a comparison with the Article 12 based approach implemented in UNEP-WCMC

(2017). However, it is observed that calculating Shannon Index by survey square and averaging these across all

survey squares of the same ecosystem typology would be a more representative indicator of MAES ecosystem

type condition. This analysis is presented in Table 6 for the UK test Case study.

Table 6 reveals that the Shannon indices are lower by approximately 1 in all ecosystem types. However, the

trends in Shannon Index generally reflect those presented in Table 4 for the 2000 and 2006 accounting period.

The exception being, for Grassland and Sparsely vegetated ecosystems (where Shannon Index reduces when

averaged across survey site grid cells, instead of increases). With respect to Table 5 for the 2006 to 2012 period,

the only difference in Shannon Index trend noted in Table 6 is for Heathland / Shrub (which decreases

marginally in Table 6, instead of increasing).

Table 6: UK Bird Shannon Index Based on 1km Survey Grid Averages (2000 - 2012)

A similar approach could clearly be applied to species richness, for an ecosystem type. Whereby this metric is

calculated using the median statistic of species richness across all cells sampled in that ecosystem type. As

species richness can be expected to scale non-linearly with sampling intensity, this could also address some of

the bias that emerges when using survey data, where sampling intensity varies considerable between monitoring

periods.

2.2. Testing Methodological Assumptions The number of individuals observed and even the count of species identified in a location is strongly affected by

the amount of effort expended in the sampling (as well as natural variation). Therefore, if sampling effort

changes substantially through time, or if there is substantial variation across spatial extents that are being

compared, then correction should be made for this. The abundance of all birds observed can be naively assumed

to scale linearly with the amount of effort. However, in practice many species flock and the detection of flocks,

is less dependent on effort and more dependent on more randomly occurring flock disturbance events.

Common Bird Species Account Using National Monitoring Data for 2006 and 2012: United Kingdom

Cropland Grassland

Heathland /

Shrub Marine Inlets Rivers / Lakes

Sparsely

Vegetated Urban Wetlands

Woodland /

Forest All Ecosystems

Total abundance

(No. individuals)

17,726,030 20,604,516 2,094,338 407,942 835,186 421,568 - 1,889,569 5,183,512 49,162,660

Number of

species97 98 85 67 73 67 - 81 90 105

Shannon's Index3.21 3.39 3.13 3.07 3.27 3.22 - 2.84 3.32 3.39

Total abundance

(No. individuals)

559,425 1,616,997 252,578 -35,143 -184,995 -14,126 - 104,605 302,845 2,602,186

Number of

species2 -2 -5 7 -7 1 - -4 2 2

Shannon's Index0.04 0.01 0.08 0.12 0.23 0.07 - -0.13 0.04 0.01

Total abundance

(No. individuals)

18,285,455 22,221,513 2,346,916 372,799 650,191 407,442 - 1,994,173 5,486,357 51,764,846

Number of

species99 96 80 74 66 68 - 77 92 107

Shannon's Index3.25 3.40 3.20 3.19 3.50 3.29 - 2.71 3.36 3.40

Close (2012)

MAES

Open (2006)

Net Change

Cropland Grassland

Heathland

/ Shrub

Marine

Inlets

Rivers /

Lakes

Sparsely

Vegetated Wetlands

Woodland

/ Forest

2000 2.55 2.56 1.77 2.20 2.72 1.67 1.22 2.49 2.46

2006 2.53 2.51 1.84 2.26 2.55 1.61 1.52 2.47 2.42

2012 2.58 2.59 1.82 2.35 2.76 1.76 1.35 2.54 2.50

MAES

Year

All

Ecosystems


17

Expressing abundance relative to the effort expended can be expected to make observations more comparable

(hence the use of population densities in Section 2.1). Standardised monitoring schemes such as the BBS and

JPSP control for field effort through time, and hence can provide reliable estimates of trends. However, there

may be nuanced effects of effort on species richness. This is because species have differences in their relative

abundance, which may vary through time. As such there tends to be a non-linear relationship between effort and

the number of species identified. There are a substantial number of methods for adjusting sampling effort to

estimate the total number of species present. For example, using species accumulation curves and non-

parametric methods based on the abundance or incidence of rare species can be a solution.

Species accumulation curves are based on fitting a curve of species richness against cumulative sampling effort.

To obtain a stable mean species richness for a given number of sampling effort, survey results are repeatedly and

randomly drawn and aggregated up the total cumulative effort (i.e., all survey results). This curve reaches an

asymptote when all species in a location or habitat are identified. Where it is evident that sampling effort has not

identified all species (i.e., an asymptote has not been reached), a non-linear regression function can be fitted to

the species accumulation curve. This function can then be used to extrapolate the sample data, and estimate what

the true species richness would be. Thompson et al., 2003 demonstrated the ‘fit’ of these functions for species

accumulation curves match survey (pit-trapping) by in excess of 95%.

Walther & Martin (2008) also review the use species accumulation curves and fitting non-linear regression

functions to estimate true bird species abundance for bird communities in the Queen Charlotte Islands, Canada.

They find that non-parametric estimators tended to perform better than species accumulation curve methods. In

particular, they find two non-parametric methods, based on the abundance (Chao1) or incidence (Chao2) of rare

species (Chao, 1984) to be the least biased and most precise methods for estimating true bird species richness

(S*). Chao 1 (S1) is calculated as the number of species in the sample observed (Sobs), with the given amount of

effort plus the ratio of the square of the number of species found with an abundance of 1 (a) to double the

number of species with an abundance of 2 (B):

S1 = Sobs + (a2 / 2B) (Equation 1) (Colwell & Coddington, 1994)

As Colwell & Coddington (1994) observe, in the absence of singletons or doubletons in a sample set Equation 1

reduces to S* = Sobs.

Chao 2 (S2) is calculated as the number of species in the sample observed with the given amount of effort (Sobs)

plus the ratio of the square of the number of species found in only one sample (L) to double the number of

species found in exactly two samples (M):

S2 = Sobs + (L2 / 2M) (Equation 2) (Colwell & Coddington, 1994)

For the Czech Republic, calculating the Chao 1 estimate suggested that not all species were being identified in

all ecosystems (Table 7). This was particularly the case for Wetlands (as defined by MAES), which were not

captured in the 2000 survey results. Interestingly, the Chao 1 result supports the notion that the large increase in

species richness observed in Grasslands between 2000 (S1 = 97) and 2006 (S1 = 124) in Table 2, Section 2.1.3.2

(i.e., +21 species) is indicative of a real increase.

However, as Table 7 shows, by 2012 the species richness observed was within 3% of the Chao estimate in all

ecosystem types other than wetlands. This provides some confidence that at the national scale these geo-

referenced Species Accounts are provided a relatively accurate reflection of true species richness for these other

MAES ecosystem types by 2012.

Table 7: Chao 1 estimates for the Czech Republic.


18

Table 8 presents the Chao 1 estimates for the UK Test Case. Table 8 reveals that the observed species richness

by MAES ecosystem type, is generally within 5% of the Chao 1 estimate for each MAES ecosystem type. The

only incidents exceeding this 5% threshold were mainly related to the 2000 survey data and comprised: Marine

inlets (only 87% of Chao 1 observed); Heathland/shrub (only 93% of Chao 1 observed) and Cropland (only 94%

of Chao 1 observed). In addition, for Rivers / Lakes only 86% of Chao 1 observed in in 2006 and for sparsely

vegetated land only 89% of Chao 1 was observed in 2012. This provides a reasonable level confidence that, at

the national scale, these geo-referenced Species Accounts provide a reasonably reflection of species richness by

MAES ecosystem type.

However, for both the Czech Republic and the UK test cases, the differences between the Chao 1 estimates and

observed species richness are comparable with the net changes observed. They also suggest that a number of

rarer common birds exist in the dataset.

It is noted that for Heathland / shrub in the 2012 UK test case, a value for Chao 1 could not be calculated as there

were singletons (a) but no doubletons (B) for certain species. This clearly reveals a weakness in the Chao 1

based estimation approach, compared to the use of parametric methods such a species accumulation curves.


19

Table 8: Chao 1 estimates for the UK

2.3. Comparative analysis with Article 12 based accounts The Species Accounts compiled using the Article 12 data reported by Member States under Article 12 of the

Birds Directive (see UNEP-WCMC 2017), was grounded in disaggregating these data on the basis of the

following assumptions:

● Bird species population data can be disaggregated by MAES Ecosystem type using the preferences

proposed by the EEA (2015).

● Bird species abundance data can be split evenly across the MAES Ecosystem types each species

prefers, regardless of the relative extent of these ecosystems and the strength of preferences).

● Given that the updated Article 12 reporting format was only established for the 2012 reporting, the

temporal trends in bird species population status can only be assessed using indicators derived from the

population trends reported by Member States. These comprise of both short (12 years prior to reporting)

and long-term (from circa 1980) trends reported for bird species by Member States.

● Bird species population data that was reported as potentially inaccurate was omitted for the calculation

of the accounts.

Comparing the accounts produced using the Article 12 reporting data with the approach presented in Section 2.1

allows an evaluation of whether common signals emerge from both approaches. Such an analysis can be

considered indicative of testing the bottom up type approach employed, using georeferenced data and the top

down approach described above. This will provide a useful insight into the potential for using the Article 12 data

based Species Accounting approach in different decision making contexts.

2.3.1. Czech Republic Comparative Analysis

In order to inform the comparative analysis, common bird Species Accounts were produced for the Czech

Republic using the Article 12 data using the approach set out in UNEP-WCMC (2017). These are presented in

Table 9. In comparison with Table 3, which presents the Czech Republic Common Bird Species Account (2006 -

2012), the following observations on key accounting items can be made:

● Table 3 provides data on 125 species across all ecosystems, whereas only 17 are represented in Table 9.

It is understood that this reflects that the Czech Republic only report on Annex I bird species in their

2012 reporting and a comprehensive set of Article 12 species will be reported on for the 2013 to 2018

reporting period.

● Substantial differences in the total abundance estimates are observed between Table 3 and Table 9 (up

to three orders of magnitude). This is likely to be due to a combination of the previously discussed over

estimation with respect to Table 3 and the above point.

● The only negative trend reported in Table 9 is in Heathland / shrub ecosystems. However, these actually

occupy a very small fraction of the Czech landscape (estimated < 0.1%) and are not captured in the

JPSP survey locations.

Table 9: Czech Republic Common Bird Species Account (Based on Article 12 Reporting Data, 2007 - 2012)

Cropland GrasslandHeathland /

Shrub

Marine

Inlets

Rivers /

Lakes

Sparsely

vegetatedWetlands

Woodland /

Forest

2000 Species richness observed 96 95 80 68 70 60 70 88

Chao 1 estimate 102 99 86 78 74 61 74 88

% Chao 1 observed 94% 96% 93% 87% 95% 98% 95% 100%


Chao 1 estimate 99 100 87 69 85 69 85 94

% Chao 1 observed 98% 98% 98% 97% 86% 97% 95% 96%


Chao 1 estimate 101 98 NC 76 66 76 81 92

% Chao 1 observed 98% 98% NC 97% 100% 89% 95% 100%

NC = Not calculable as no doubletons in the survey data for the given MAES Ecosystem Type


20

2.3.2. United Kingdom Comparative Analysis

Following the same approach outlined for the Czech Republic, common bird Species Account derived using the

Article 12 data area presented for the UK in Table 10.

Table 10: UK Common Bird Species Account (Based on Article 12 Reporting Data, 2007 - 2012)

In comparison with Table 5, which presents the UK common bird Species Account (2006 - 2012) using the BBS

data, the following observations are made:

● Table 10 and Table 5 provide relatively similar counts for species richness and abundance across all

ecosystems. This is to be expected as the BBS is the UK data source for the Article 12 reporting also.

● At the ecosystem specific level, Table 5 reveals that common bird species are more generalist in their

use of ecosystems than Table 10 suggests. Generally, the species richness metrics presented in Table 7

are 2 to 3 times higher than in Table 10. This reflects that birds are only afforded up to a maximum of 3

MAES linkages in EEA (2015).

● Likely, as a result of the above point, the Shannon index is consistently higher in Table 5 across

individual ecosystems. However, it is similar when calculated for all ecosystems. This suggests

differences are emerging from the disaggregation procedure of the Article 12 data.

● Abundance estimates by ecosystem type are generally the same order of magnitude in Tables 5 and 9.

The significant exceptions being, grassland (an order of magnitude higher in Table 5) and woodland

and forest (an order of magnitude lower in Table 5).


21

● Comparison of the net changes in Table 5 with the ‘Trends in Status’ reported in Table 10 does not

identify a consistent set of signals for how bird species status is evolving by ecosystem type. However,

at the aggregated level, a general consistency is observed. This may reflect that in the Article 12

approach bird species are allocated an affinity to a maximum of 3 MAES ecosystem types (see Roscher

et al., 2015). Hence, these inconsistent signals may be linked to reflect that common birds are more

‘Generalist’ in their use of habitat types.

2.4. Conclusions The Species Accounts produced for the Czech Republic and the UK demonstrate that it is possible to construct

geo-referenced bird Species Accounts by MAES ecosystem type using national bird monitoring data. However,

for the UK Test Case it was not possible to link the BBS survey data with urban ecosystem trends in bird species

biodiversity. For the Czech Republic case study, data gaps were also identified for Heathland / shrub and

sparsely vegetated areas. However, these comprise only a small proportion of the Czech Republic landscape. The

Chao testing undertaken also suggests that the survey data is successful in identifying common bird species in

MAES ecosystem types. Survey observation for species richness are generally in excess of 90% of Chao

estimates in 2012 for all ecosystems, with the exception of inland wetlands in Czech Republic and sparsely

vegetated land in the UK. Overall, the Chao 1 results suggest there are some rarer common bird species being

identified in the survey data in both countries.

The UK Test Case identified that common birds are generally quite generalist, revealing high species richness in

a number of different MAES ecosystems. As such there may be a case for following the farmland and woodland

common bird index approaches, and focusing on a set of more specialised common birds to get better signals of

changes in ecosystem condition for accounting. This should be a key consideration in any further development of

these Species Accounts.

Comparative analysis suggests that the disaggregation procedure inherent in the Article 12 based approach is too

narrow. This results in consistently lower species richness and Shannon index results by MAES ecosystem type

for the UK test case. This indicates that investing in the capacity to collate, harmonise and synthesise EU wide

bird survey results is required to yield the data needed for regular EU terrestrial ecosystem condition accounting.

The comparative analysis reveals that for the Czech Republic, national monitoring data is available for more

common bird species than reported on under Article 12 of the Birds Directive. It is understood that this reflects

that the Czech Republic only report on Annex I bird species in their 2012 reporting, a very small subset of bird

species concerned by Article 12 reporting. A comprehensive set of Article 12 species will be reported too for the

2013 to 2018 reporting period.

In broad terms, the BBS can be considered to provide bird species counts for 1km squares. While the JPSP

provides transect data derived from a series of point counts, and our analysis assumes that each of these point

counts represents bird counts for a single square ha (with the point assumed to be at its centre). The comparative

analysis and cross referencing with other statistics on birds species abundance in Europe suggests the latter

approach results in over-estimates of abundance. This is likely to reflect that birds will be recorded when

surveying at a point even if they are in excess of 50m away from the surveyor. Hence, these over-estimations

may arise from assuming that counts come from a smaller area than they really do. This is could also be

exacerbated where the Czech dataset includes observations of birds in flight.

Overall, the procedure for estimating abundance in absolute terms delivers fairly volatile results in both the

Czech Republic and UK case studies. The use of relative abundance indexes, which are commonly derived from

bird survey data, may ameliorate this. These are often combined with statistical modelling approaches to deal

with missing data when locations are inconsistently monitored (e.g., Trends and Indices for Monitoring Data

(TRIM) program; (Pannekoek and van Strien, 1996). These could inform an alternative approach to construct

Species Accounts for ecosystem types, based on calculating national or supranational ecosystem specific

population trends. This approach would yield indices similar to the overall common bird indices countries report

on, which would show population trends of species in a given ecosystem/habitat type. This could also be

integrated with the above proposal with respect to grouping common birds that have particular ecosystem

preferences.


22

It was noted that calculating bird species diversity indices at grid cell scale and aggregating these results, may

provide a better indication of ecosystem condition (as per Table 6). However, this would not facilitate the

comparative analysis with the Article 12 approach. In absolute terms, the Shannon Index for the UK data was

found to vary significantly when calculating average Shannon index results across grid cells, rather than using

aggregate data. However, trends in the Shannon Index overtime were reasonably consistent, with one or two

exceptions.

It is acknowledged that the Bird Species Accounting methodology has naïve aspects. For example, the issues

associated with difference in the detectability of different species because of intrinsic species level differences,

variations in habitat in which species are observed and species abundances are ignored. Accounting for this is

important, as detection differences could lead to erroneous patterns in community diversity metrics. It is also

acknowledged that the community measures used here, species richness and Shannon index, do not capture

information about turnover of species in the community, for example as a result of the introduction of invasive

species. It is not obvious how this should be treated in accounts. Measures of similarity such as Sorensen index

would inform about compositional turnover through time, however, it is not straightforward to associate this

with improvement or deterioration of habitat quality. Addressing this aspect will also be critical to future

accounting building on this approach.

Methodological advancements to be considered in future analysis might include hierarchical mixture modelling

of individual species site abundances, taking into account the probability that the site is occupied by the species.

Other advancements may consider the relationship between the species abundance and covariates, and variation

in detectability as a function of species traits, site-level covariates and abundance (building on for example Kery

and Royle, 2010). Inference from these models could provide extrapolation of the estimated total individual

species abundances across space. These models could benefit from the upcoming European Breeding Bird Alas 2

(EBBA2) project (https://www.ebba2.info/), as modelled probability of occurrence will be available for whole

Europe for around 300 species at 10x10 km scale.

The scale of the analysis conducted also has the potential to neglect land cover types that are rare, or tend to be

found in small spatial extents. If these land cover types are not associated with broader land cover types, then

their effect will not be captured. Further assessment of the effect of observations in small-scale or small habitat

patches, which are erroneously associated with larger land cover types should be conducted.

In terms of delivering policy insights, the bird based Species Accounts provide a useful conduit to management

options to progress towards Target 1(ii) of the EU 2020 Biodiversity Strategy (50% more species assessments

under the Birds Directive show a secure or improved status). The community statistics on bird species, provide

potentially useful indicators of the environmental health. These can also be used to support decision making in

pursuit of better, more integrated management of natural capital. By organising data on birds from the MAES

ecosystem perspective, alongside information on extent, pressures, services and land use, different land

management, trade-offs can be explored that could inform better decision-making with better outcomes for the

condition of ecosystems for birds. The possibilities in these regards are improved using spatially referenced data,

rather than relying on broad ecosystem preferences of birds (i.e. as set out in Roscher et al. (2015)). Moving to a

fully spatial approach supported by a 1km core accounting grid that supports detailed spatial analysis, would

realise the full potential of these possibilities. This would also better inform on the interactions between

ecosystems and economic sectors. Organising data on bird species abundance according to functional traits

within the accounts, could also make the links to ecosystem services tangible in a management context. Options

in these regards are explored further in Section 3.

The EU 7th Environmental Action Programme (EAP) and Target 2 of the Biodiversity Strategy call an

enhancement of natural capital and restoration of ecosystems and their services. The Species Accounts provide a

pathway for integrating the rich body of data collated under schemes such as the PECBMS with wider ecosystem

an economic data and presenting this using a MAES perspective. This is the key advantage of the ecosystem

accounting approach, whereby more integrated environmental-economic analysis can direct macro-level decision

making that delivers better environmental outcomes. This integrated approach also delivers a more complete

picture of ecosystem capital (and related ecosystem services), as a complement to economic measures of a

nation’s wealth. This approach is also consistent with the established national economic accounting approaches

regular used in planning.

https://www.ebba2.info/


23

It is stressed that the Species Accounts presented for the Czech Republic and UK are experimental test cases.

Their purpose is to test different methodological approaches and identify possibilities for informing accounts

relevant to biodiversity at the EU scale. A key outcome of the test cases is the identification of constraints with

respect to sampling procedures that result in certain bias, which should be addressed. This is more significant for

the Czech Republic test case, given the reduced sampling intensity and free-choice of sampling location.

Modelling approaches offer a way to extrapolate species distribution or community levels statistics to address

data gaps and potential bias. These are explored further in Section 3 for the UK Test Case and in the context of

moving to a flexible spatial ecosystem accounting approach at the EU scale.

It is also noted that it is usual practice for national coordinators of monitoring programmes to only report on a

subset of common birds considered characteristic of their countries’ avian communities. For the accounts

presented, all common birds are considered – this allows a comparison with the Article 12 outputs from 2017.

However, in practice, out of the 167 common birds considered by the PECMBS, the BTO only provides data on

111 species (European Bird Census Council, 2016a), and the CSO 118 species (European Bird Census Council,

2016b). Generally, common bird species are omitted where they are rare breeders, wintering birds or there is

insufficient data. As such, it is likely to be necessary to either adopt these national sub-setting of common

species, or establish regional subsets when moving to an EU wide bird based Species Account. This is also likely

to improve the convergence between the species observed via the surveys, and the Chao 1 estimates by

ecosystem type.


24

3. Experimental Integrated

Biodiversity Account The objective of this task (Task 2.4) is to develop a test case and methodological roadmap for an experimental

integrated biodiversity account based on data contributions for a number of species and information on

ecosystem extent. The task builds on the insights gained from compiling bird based Species Accounts in Section

2 and a database of pollinator data compiled by the Institute of European Environmental Policy (IEEP, presented

as Appendix B). These are combined with the ongoing review of MAES Pilot Study spreadsheets on ecosystem

condition indicators by the EEA, and other readily available EU information on species. The test case is based on

a minimum spatial approach, whereas the roadmap elucidates on possibilities for moving to a fully spatial

approach, and the steps that would be required. In addition, the application of a risk register based approach for

considering ecological risks in the context of the SEEA EEA is tested. This draws on the integrated biodiversity

account compiled using a minimum spatial approach, and key EU policy targets to develop a tool for policy and

land use decision-makers.

3.1. Organising spatial data on biodiversity using the SEEA EEA Ecosystem accounting is predicated on the integration of different information on ecosystems and the economy.

As set out in the SEEA-EEA TR (UN et al., 2018), the conceptual units for such integration comprise of

spatially delineated contiguous areas of the same ecosystem type (Ecosystem Assets). Here emerges an

interesting mismatch between the CBD definition of biodiversity and the treatment of biodiversity within the

SEEA-EEA. In the CBD definition, ecosystem diversity is a component of overall biodiversity, whilst the

SEEA-EEA adopts the CBD definition it still treats biodiversity as a characteristic of ecosystem condition.

Therefore, in consideration of the CBD definition, an integrated biodiversity account requires the foundation of

information on ecosystem diversity that ecosystem extent accounts provide. Indeed the Ecosystem Extent

Account is considered highly relevant to biodiversity accounting (UNEP-WCMC, 2015).

3.2. Testing a Minimum Spatial Approach The minimal spatial approach commences from an aggregated (e.g., national) accounting perspective and

provides a broad set of information on ecosystems to support decision-making. For an integrated biodiversity

account for the EU, the first step is determining the extent of different MAES ecosystem types within the EU.

This is available from the Ecosystem Extent Accounting report produced by the EEA (2018, draft). Drawing on

this draft report, ecosystem extent measures in km2 have been included in the account, presented as Table 11.

Given these measures of MAES ecosystem extent are grounded in the CLC editions, the account covers the

period of the two most recent CLC editions (2006 and 2012). Due to the nature of the CLC data, Table 11

represents terrestrial ecosystems only.

With the information on ecosystem extent in place, metrics of species-level diversity and abundance (or suitable

proxies) can be integrated by ecosystem type to provide thematic integrated biodiversity accounts. In turn, these

can be used to inform on ecosystem condition with respect to supporting biodiversity. The first source of data

reviewed were the spreadsheets on identified condition datasets for the MAES pilot studies for agroecosystems,

freshwater ecosystems, forest ecosystems, marine ecosystems and nature (covering heathlands and shrub,

sparsely vegetated land and; wetlands). These data are currently being reviewed and developed in parallel work

at the EEA, to identify which parameters are suitable for immediate use for ecosystem condition accounting. The

following indicators in the spreadsheets were considered suitable for integration into Table 11: structural

ecosystem attributes based on species diversity and abundance; and, structural ecosystem attributes monitored

under the EU nature directives. These indicators are considered well-aligned with species-level metrics. Of these

indicators, only those underpinned by data suitable for immediate use were considered potentially suitable for

inclusion in Table 11. Regarding data on pollinators collated by IEEP (see Appendix B), those considered

relevant to the EU scale and amenable to presentation from a MAES perspective, were reviewed as potentially

suitable for inclusion in Table 11. A brief internet search for EU scale plant and animal surveys was completed


25

to identify any additional key data in these regards. A compressive collation of wider biodiversity data for the

EU is also provided in Siriwardena and Tucker (2017).

These data are collated as Appendix C, with key metadata on location, extent of coverage, resolution, time span,

source, data type (spatial or non-spatial). Each dataset was then ranked as follows:

1. Data organised by: MAES Ecosystem type; of directly relevant to a MAES ecosystem type (e.g.

Farmland Bird Index as an indicator of cropland condition – although it is noted this indicator also

cover birds observed in pastoral habitats)

2. Data requiring some manipulation such as GIS processing or other processing, to align with MAES

ecosystem types

3. Data which cannot be assigned to a MAES ecosystem type.

Those that were ranked as “1”, are included in the integrated biodiversity account presented in Table 11 (they are

also highlighted in green in Appendix C). Table 11 was then compiled using available data by MAES ecosystem

type for 2006 (open stock) and 2012 (close stock) and the net change calculated for these species relevant

accounting items. Where data was not exactly aligned with these opening and closing years, the closest year for

which data was available was selected (e.g., 2010 and 2016 for the Water Framework Directive (WFD) reporting

cycles). The accounts presented in UNEP-WCMC (2017) are used to inform the relevant bird species statistics

for the Article 12 based data items.

The integrated terrestrial biodiversity account shows a reduction on the extent of cropland, grassland, Heathland

/ shrub, Marine inlets and Sparsely vegetated land between 2006 and 2012 (albeit <0.25% in relative terms).

Interestingly, this is associated with a reduction in the value of all the species related metrics in Table 11,

although given the very marginal nature of the reductions in ecosystem extent, this is considered to be

coincidental.

At the EU level, both changes in the farmland birds index (-4.45) and the Article 12 ‘Overall Trends’ index (-

20.48) suggest a loss of cropland condition between 2006 and 2012. Similarly, large reductions in the value of

the EU grassland butterfly indicator (-14.35) and the Article 12 ‘Overall Trends’ index (-22.22) indicate a loss of

Grassland condition over this accounting period. UNEP-WCMC (2017) reports on the method used for

calculating these trends using information on the short and long term trends reported for bird species by Member

States under Article 12. In Table 11, the Article 12 ‘Overall Trends’ index indicates a loss of condition for

Heathlands and shrub (-16.96) and marine inlet (-13.16) ecosystems between 2006 and 2012.

The data derived from reporting under the WFD for River Basin Management Plans (RBMP) also supported the

notion of a loss of condition for marine inlets (based on transitional waters). The percentage of these water

bodies achieving good ecological status for all biological quality elements, decreased by 14% between 2006 and

2012. For fish only, this drop was less, with 12%. For River / Lake ecosystems, the WFD based indicators are

relatively stable between 2006 and 2012, although the Article 12 ‘Overall Trends’ index suggests a possible

improvement in condition for birds (+5.81). Both the forest birds index (+2.87) and the Article 12 ‘Overall

Trends’ index (+12.64) suggest an increase in the condition of woodland / forest ecosystems between 2006 and

2012. For wetland and urban ecosystems, the Article 12 ‘Overall Trends’ index is essentially stable between

2006 and 2012.

Table 11 identifies a number of data gaps. There is a heavy reliance on bird based statistics, the Article 12 Birds

Directive data is available for all MAES ecosystem types, but no other species related indices are available to

complement this: heathland/shrub, sparsely vegetated, urban or wetlands. Whilst the farmland and forest bird

indexes support the Article 12 ‘Overall Trends’ index, they still represent bird species. It should be noted that

these indexes are based on data from 26 Member States, rather than the full EU-28 set of Member States.

Similarly, the EU Grassland Butterfly indicator is based on data from a limited number of 22 Member States.

With respect to the WFD based indicators, significantly more waterbodies were assessed in 2016 than in 2010

(see EEA, 2018). As such the 2010 and 2016 assessments are not fully comparable, hence the trend information


26

in Table 11 needs to be interpreted with some caution. The EEA is currently developing a consistent

methodological approach that will address this issue, and increase confidence in using this important dataset for

freshwater ecosystem condition accounting. The WFD reporting data also benefits from an established spatial

infrastructure, connected to the requirement of delineation of water bodies, as described in EEA (2018a). As

such, this data could support a spatial approach to freshwater ecosystem accounting in the EU.

With respect to the stock estimates, it has previously been observed that there is Article 12 data available from

2006. However, the upcoming publication from the 2013 to 2018 reporting period will provide the necessary

data to calculate changes in stocks, for a number of the Article 12 data items shown in Table 11. Using Article

17 data provides a potential pathway to expand the taxonomic coverage of the integrated biodiversity account.

Methodological tests have been progressed via the EEA, to develop a Species Accounting approach grounded in

the Article 17 data.

There are a number of datasets identified via the MAES Pilot studies that could quite readily be processed and

integrated into Table 11. Whilst no data for % ecosystem covered by Natura 2000 was readily identifiable for

2012, this will shortly be available from the upcoming EEA extent account ‘working report’ (chapter 5).

Similarly, for protected area coverage these could also be rapidly generated. However, whilst increasing trends

in these types of land management indicators would assist in tracking progress towards protecting the EUs

natural capital, integrating species-level biodiversity data is key for credible ecosystem condition accounts. This

requires establishing further processing activities to make the data reported under the EU directives suitable for

accounting, or in collating and processing more primary species monitoring data from across the EU. Options

with respect to the latter are now explored in Section 3.3.


27

Table 11: Integrated Terrestrial Biodiversity Account (2006-2012)

Integrated Terrestrial Biodiversity Account (2006 to 2012)

Cropland Grassland Heathland / Shrub Marine Inlets Rivers / Lakes Sparsely

Vegetated

Urban Wetlands Woodland / Forest

Extent (km2) 2,036,471 652,873 279,699 28,598 141,502 346,798 232,739 129,149 2,010,199 5,858,028

EU grassland butterfly indicator 72.51

Common Bird Index 95.02

Farmland Bird index 76.25

Forest Bird Index 94.53

% Ecosystem Covered by Natura 2000 7% 16% 25% 11% 28% 11% 2% 29% 21% 15%

Article 12 Birds Directive (All birds Shannon

Index)ND ND ND ND ND ND ND ND ND ND

Article 12 Birds Directive (All birds Total

Abundance)ND ND ND ND ND ND ND ND ND ND

Article 12 Birds Directive (All birds Species

Richness)ND ND ND ND ND ND ND ND ND ND

Article 12 Birds Directive (All birds Aggregate

Status Index)ND ND ND ND ND ND ND ND ND ND

Article 17 Habitats Directive TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST

WFD (Fish % BQE at GES) 2010 77% 47%

WFD (Overall % BQE at GES) 2010 61% 45%

Extent (km2) -4,947 -1,476 -669 -50 985 -268 5,737 59 629 0

Extent (%) -0.24% -0.23% -0.24% -0.17% 0.70% -0.08% 2.46% 0.05% 0.03% 0

EU grassland butterfly indicator -14.35

Common Bird Index -2.12

Farmland Bird index -4.45


% Ecosystem Covered by Natura 2000 ND ND ND ND ND ND ND ND ND ND

Article 12 Birds Directive (Overall trends) -20.48 -22.22 -16.96 -13.16 5.81 -3.37 2.04 0.68 12.64 -1.32


WFD (Fish % BQE at GES) -12% 0%

WFD (Overall % BQE at GES) -14% -1%

Extent (km2) 2,031,524 651,397 279,030 28,548 142,487 346,530 238,476 129,208 2,010,828 5,858,028

EU grassland butterfly indicator 58.16

Common Bird Index 92.90

Farmland Bird index 71.80


% Ecosystem Covered by Natura 2000 ND ND ND ND ND ND ND ND ND ND

Article 12 Birds Directive (All birds Shannon

Index)2.93 2.32 3.13 2.46 3.12 3.14 2.92 3.22 3.66 4.18

Article 12 Birds Directive (All birds Total

Abundance)2.63E+08 9.58E+07 9.73E+07 2.02E+06 2.68E+07 4.15E+07 3.09E+08 2.42E+07 4.70E+08 1.33E+09

Article 12 Birds Directive (All birds Species

Richness)83 81 112 38 155 178 49 147 174 454

Article 12 Birds Directive (All birds Aggregate

Status Index)71.74 62.67 66.49 63.51 73.90 60.14 84.44 73.02 82.68 70.68


WFD (Fish % BQE at GES) 2016 65% 47%

WFD (Overall % BQE at GES) 2016 47% 44%

ND = No data available from that period; TEST = Methodological tests in going via the EEA to use this data for accounting

Species diversity and abundance

EU nature directives



MAES

Close (2012)

All Ecosystems

Open (2006)



Net Change


28

3.3. Developing a Fully Spatial Approach Shifting to the fully spatial approach requires the availability of georeferenced data, and an appropriate

underlying spatial data infrastructure, to allow integration of different georeferenced datasets. In order to support

the compilation of spatial Ecosystem Accounts for Europe, it is proposed to use the EEA reference grid for

Europe at the 1km resolution as a core accounting grid.6 This provides the spatial infrastructure for integrating

georeferenced species data with information on ecosystem extent and other accounting themes, thus facilitating a

spatially integrated approach to biodiversity accounting.

3.3.1. Modelling test using BTO data aligned with the 1km Accounting Grid

Using the data described in section 2.3.1, the following was tested: how accurately bird biodiversity measures

could be extrapolated from the known locations, to other locations using a range of predictor variables

standardised to the 1km accounting grid. To do this, the BTO data for the year 2006 was subset into two

datasets, a training dataset, comprising of 75% of the data (2650 grid cells), and an evaluation dataset made up of

the remaining 25% of the data (884 grid cells). The training data was used to construct explanatory models of the

relationship between bird species biodiversity, and co-located predictor variables. These models were then

applied to the evaluation data to make predictions of bird species biodiversity, given the predictor variable values

at the grid cell locations within the evaluation dataset. The predictions were then evaluated against the observed

data in the evaluation dataset, to quantify the accuracy of the spatial extrapolation.

3.3.1.1. Bird species biodiversity variables

Modelling how the abundance of individual species will vary across locations as a function of the spatial

characteristics of those locations, is challenging for several reasons. First, the population in a single 1km2 grid

cell is likely to be strongly influenced by the surrounding neighbourhood, because most populations of birds,

especially those with larger body size, experience the environment at a larger scale than 1km2. Second, the

population in a grid cell will fluctuate for reasons that are hard to relate to the spatial characteristics of the

location, for example, disease outbreaks or other ecological interactions (e.g. immigration or native population

growth of species that predate on the focal species). Third, it is practically very challenging to gather data

covering all the characteristics of a location that govern population changes (e.g., habitat type, land use, pollution

and invasive species). Therefore, there will always be population variations that cannot be explained with the

available data.

Whilst species abundance is a key aspect of biodiversity, this typically requires modelling abundance on a

species by species basis (e.g., Siriwardena et al., 2012). Furthermore, community based metrics are established

as relevant indicators for ecosystem condition, although it is noted that the relationship is not quantitative and, as

discussed in section 2.4, there is no clear definition of the characteristics of habitat in good condition. Therefore,

it was investigated whether measures of species-level biodiversity of a grid cell, rather than abundance per se,

are more predictable given grid cell spatial characteristics. Specifically, modelling common bird species richness

or Shannon Indices (as introduced in Section 3), was investigated. Hulme and Siriwardena (2010), demonstrate

an approach to spatially extrapolate Simpsons Diversity statistics, calculated from BTO data using the Land

Cover Map for the UK. Hulme and Siriwardena (2010) strongly suggest that modelling of community based

metrics might not be accurate, because responses are species specific. Therefore, an explicit evaluation of the

predictive ability of the modelling is provided using sample prediction. These approaches were carried out using

EU data on land cover and other predictor variables.

3.3.1.2. Predictor variables

In addition to the CLC data used to summarise the BTO data by MAES ecosystem type (as described in section

2.3.1), spatial information was gathered on biophysical and socio-political properties of 1 km grid cells across

the EU. These data and their sources are summarised in Table 12, and have been shown to be significant in

explaining species diversity in other studies (e.g. Luck, 2007; Rahbek et al., 2007; Newbold et al., 2015).

Table 12: Other predictor variables and their data used in modelling approach

6 This grid is now standardised at several spatial resolutions via INSPIRE. See EEA, 2017 for further details in the reference

grid.


29

Dataset Description Source

Elevation Elevation in each 1km2 grid cell. https://www.eea.europa.eu/data-and-

maps/data/digital-elevation-model-of-

europe

Human

population

density

Total population per km2 for the year 2006 https://ec.europa.eu/eurostat/web/gisc

o/geodata/reference-data/population-

distribution-demography/geostat

Temperature Annual mean surface temperature for 2006.

Calculated from monthly mean data from

MODIS (Moderate Resolution Imaging

Spectroradiometer), at a resolution of 0.1

decimal degrees. Data was resampled using

bilinear interpolation to the EEA 1km2 raster.

https://neo.sci.gsfc.nasa.gov/view.php

?datasetId=MOD11C1_M_LSTDA&

year=2006

Natura 2000

sites

Site type category for each Natura 2000 site. https://www.eea.europa.eu/data-and-

maps/data/natura-9

The values for each of the predictor variables listed in Table 12 were extracted for the grid cells in which BTO

observations were made in the year 2006. These predictor variables were combined with the CLC Level 3

classifications, for the grid cell calculated using the majority rule described in Section 2.1.5.

3.3.1.3. Modelling

Two modelling methods were used to test for spatial extrapolation potential. In the first, random forest models

were fitted to the training data for species richness and Shannon index. Random forest models are a type of

supervised machine learning, in which an ensemble or “forest” of decision trees are generated using the predictor

variables to predict the response variable. In the second method, simple multiple regression models of the

response variable were fitted as a function of the predictor variables, without including interactions between the

predictors. The implications of land cover classification resolution were also tested (i.e. resolving to CLC level 3

classes or using the broader MAES ecosystem classes). This was performed by fitting two sets of models, one

classifying each 1km2 pixel with the CLC level 3 class with the greatest area in each pixel, and the other using

the MAES type with the greatest area.

Natura 2000 site status was found to explain very little of the variation in species richness or Shannon index in

all models. This is likely to be because the majority of BTO observation grid cells were outside Natura 2000

sites.7 This variable was therefore dropped from the models. The importance of other explanatory variables is

shown in Table 13.

Table 13: Predictor variable importance in the random forest modelling approach

Predictor variable Variable importance – Species Richness

(% increase in mean squared error

when variable is permuted)

Variable importance – Shannon

Index (% increase in mean squared

error when variable is permuted)

Human population

density 37.0% 27.2%

Temperature 38.3% 21.2%

Elevation 27.6% 19.4%

Corinne Land Cover

Class Level 3 31.8% 14.6%

7 The EEA indicate this is not surprising as the UK has one of the lowest terrestrial coverage by Natura 2000 sites (ca. 9%)

compared to, for example, e.g. Slovenia has a Natura 2000 coverage of ca. 30%.

https://www.eea.europa.eu/data-and-maps/data/digital-elevation-model-of-europe



https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat



https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD11C1_M_LSTDA&year=2006



https://www.eea.europa.eu/data-and-maps/data/natura-9

https://www.eea.europa.eu/data-and-maps/data/natura-9


30

3.3.1.4. . Results and methodological discussion

When tested against the evaluation data, for both species richness and Shannon index, the root mean squared

error was lower for the random forest models than for the linear models. Consequently, the forest model is

preferred as the more accurate model. The correlation between predictions and observations for the forest model

was also reasonably good. It was found that models fitted using the more resolved CLC classes to describe land

cover in each 1km2 pixel were more accurate (r2 = 0.66 for species richness, 95% c.i. = 0.62-0.69; r2 = 0.65 for

Shannon index, 95% c.i. = 0.61 - 0.68) than those using MAES ecosystem class (r2 = 0.58 for species richness,

95% c.i. = 0.54-0.62; r2 = 0.56 for Shannon index, 95% c.i. = 0.51 - 0.60). The r2 value of 0.66 for the CLC land

cover model implies the model is explaining around 66% of the spatial variation in species richness of common

birds observed in the BBS survey data for 2006.This correlation is presented graphically in Figure 6, which

shows the relationship between the species richness or Shannon index. This was observed by BTO volunteers

undertaking the BBS, and predicted by random forest models using the spatial characteristics of the locations in

which observations were made (as detailed in Table 12). The grey line indicates exact correlation, if the models

were perfect the points would all lie along this line. In both panels, the observation data plotted is independent

from the data used to train the random forest models. The performance of the forest model is indicative that

spatial extrapolation of community level bird species statistics from observation data, might be feasible with

sufficient data and with methodological refinement.

However, there are some caveats to the findings. Firstly, machine learning approaches have a tendency to over

fit the data, being trained against reducing their generality for spatial extrapolations. It should also be noted that

random forest models do not extrapolate against new prediction data that is outside the bounds of the training

data used to build the model. For the BTO dataset explored here, extrapolation seemed feasible. However, as the

predictor variables in the evaluation datasets lay within the bounds of that of the training data, this concern

should be taken into consideration and other machine learning approaches. For example, neural networks, that

are able to extrapolate against new data or more general models (e.g., more sophisticated linear regression)

should be explored further.

Secondly, the modelling methodology employed here ignores spatial autocorrelation, that observations close to

each other are more likely to be similar than those that are further apart. Developing the modelling approach to

incorporate information on autocorrelation should be a priority, as this is essential to capturing the spatial signals

on bird species diversity (and by extension ecosystem condition). This is implicit in BBS survey data. It is

acknowledged that abundance measures, in particular, can vary substantially over small spatial scales because of

habitat variation. So the consideration of spatial autocorrelation may be important for community metrics. The

CLC dominance rule applied in this analysis is also likely to increase spatial autocorrelation, by metaphorically

averaging out discontinuities between dominant land cover types. Other classification methodologies will likely

need to be explored, along with the consideration of spatial autocorrelation in modelling.

Despite the fact that the correlation presented in Figure 6 is moderately high, especially considering that the

response variables in both cases are effectively continuous, the results show systematic errors with over

prediction in low locations of low bird species biodiversity, and under prediction in locations of high

biodiversity. The results also indicate that there is greater uncertainty associated with the prediction of species

richness and Shannon index, in locations with lower bird species biodiversity. In addition to considering

autocorrelation, incorporating additional data on environmental quality and pressures, including land-use, in the

model may help address this. Nonetheless the results are encouraging given the crudeness of the explanatory data

incorporated in the model, relative to the complexity of real world ecological responses.


31

Figure 6: Correlation between predictions and BBS observations

The moderate success in modelling Shannon index values also gives some evidence that species abundances

might be approximated. However, as discussed below, to do this will require better explanatory data and more

sophisticated modelling techniques. These challenges for spatial modelling of farmland bird abundance have

been explored using BTO data (see Siriwardena et al., 2012).

Siriwardena et al., (2012) identifies that the internal configuration of land cover in survey squares (termed

landscape variables) is a strong predictor of different farmland species abundance, even in squares dominated by

arable land cover. After these landscape variables, more detailed data on agricultural land use (specifically

cropping type) and field boundary variables (e.g., length of hedges, ditches and woodland edges), were also

found to be significant in explaining variation in individual farmland bird species abundance. For testing the fit

of their models, Siriwardena et al., (2012) used a similar approach to that presented in Figure 6. They generally

found correlations ranging between 0.3 and 0.6 for predicting the abundance of different, individual species. This

indicates that while a considerable amount of the variation in the abundance of each species was explained,

considerable variation also remained unexplained. However, it is noted that the research focused specifically on

a selection of farmland birds in arable ecosystems, such models achieve more explanatory power when

considering a wide range of common bird species across different ecosystems. Of course, the specificity of the

status of these larger groups of common birds as indicators of condition for distinct ecosystem types, needs to be

considered further.

3.3.2. Options for moving to EU scale full spatial approach (Roadmap)

The exploratory modelling described in Section 3.3.1 was encouraging for the idea of using modelling

techniques to spatially extrapolate biodiversity data to locations where observations are yet to be made, on the

basis of CLC level 3 classes. Whilst these classes are considerably narrower than the broad MAES typology, it is

also acknowledged that each class is likely to contain a number of different habitats that will be more, or less,

suitable for different species. As such, implementing a roadmap for moving to full spatial approaches at the EU

scale, will require much greater depth of exploration to ensure that such modelling provide a realistic picture of

how bird species diversity varies spatially across the EU. Further understanding is also required on how this is

linked to the availability of different habitats, and the relative magnitudes of key pressures, such as human

population (as per Table 12). This will require combining a more extensive set of bird and environmental

datasets, and more rigorous assessment of methodologies.


32

Firstly, the evaluation of spatial extrapolations must be done more extensively. Initially, BTO observational data

for the years 2000 and 2012 should be used to test whether these datasets provide similarly encouraging signals

for spatial extrapolation. Subsequently, it will be essential to carry out analogous studies with observational data

from other countries which undertake bird censuses as part of the Pan-European Common Bird Monitoring

Scheme. This modelling can build hierarchically from within country explorations to an EU (or European) scale.

At this scale, it might be important to account for differential effort in observational sampling, as discussed in

Section 2.4. Previous studies have compiled large amounts of data from the PECBMS. For example, Gamero et

al. (2017) collated estimated bird counts from 31,319 geo-referenced bird monitoring sites from 23 EU

countries, for their analysis of the effect of Natura 2000 site designation and management plans on farmland bird

abundance trends. In related research, Pringle et al. (2017) also reviewed bird monitoring schemes considered

for use in integrated assessment of the effect of agricultural land use on farmland birds.8 The characteristics of

these bird monitoring data are summarised in Table 13.

Secondly, it is important to consider the relationship between ecosystem condition and biodiversity variables

more comprehensively, in the context of what is feasible at the large scale. For example, is species richness

sufficient for ecosystem condition accounts, or are abundance weighted measures such as Shannon index

required, and what are the limits of potential data in this regard? Alternatively, if a closer link is desired with

ecosystem function and the services that these underpin, could functional attributes of the ecological community

be of more interest? Some ideas building on these second questions are discussed in Section 3.4. Depending on

the data available, combinations with functional trait data could be used to produce observation based functional

diversity estimates, which could also be tested for spatial extrapolation.

Thirdly, the methodological approaches employed for spatial extrapolation should be rigorously explored.

Siriwardena and Pringle (2017) reviewed methodologies for assessment of potential agriculture-related drivers

on the status of habitats and species. Further work should evaluate whether these methods are applicable for the

community-based measures of species richness and Shannon index, which is considered in this Section. In

particular, the issues of differential detectability of species, as discussed in section 2.4, should be a priority for

further modelling of community-based measures. A class of models that was not considered by Siriwardena and

Pringle (2017) is Generalised Dissimilarity Models (GDMs) (Ferrier et al., 2004, 2007). GDM approaches are

specifically designed to explain how community-based biodiversity variables respond to the spatial

characteristics of the landscape. Dissimilarity provides a statistical insight into the potential complementarity of

different ecological communities in the landscape, as such GDM approaches provide useful indicators of the

potential for landscape scale ecosystem multi-functionality within accounting areas. These approaches should be

explored in the future at the EU scale. This can draw on the ecosystem accounts developed for the San Martin

region of Peru, which included biodiversity accounts developed using GDMs (Conservation International,

2016).9

Finally, higher resolution explanatory variables must be collected and incorporated in the analysis. Biophysical

information could be expanded by incorporating higher resolution land cover, to further characterise the

configuration of ecosystems in 1km grid cells. The forthcoming release of CLC+ by the EEAs Copernicus Land

Services offers key opportunities in this regard. Higher spatial accuracy to the extent that biodiversity can be

associated with rare or small habitat fragments, will be important for better resolving the condition of

ecosystems. Vegetation properties such as Leaf Area Index (LAI) or normalized difference vegetation index

(NDVI) could also be integrated into modelling. This can be used in conjunction with state of the art remote

sensing of leaf phenology, vertical vegetation structure and habitat fragmentation. There are key products that

could also be provided via Copernicus land services in these regards, although it has not been possible to test

their application within the scope of this project. Ecological suitability could also be further informed by

information on soil characteristics.

For example the European Soil Database (ESDB), and ecological data on invasive alien species presence can be

used. As can data from the European Alien Species Information Network (EASIN,

http://alien.jrc.ec.europa.eu/SpeciesMapper). As the EASIN website can provide 10 x 10 km gridded counts of

8 These studies are reported in Siriwardena and Tucker (2017). However, the authors their underlying data is unlikely to

publically available and these authors should be contacted with respect to evaluating any possibilities for using these data in

other contexts 9 Further detail of the GDM approach employed in San Martin are provided in English in (Grantham et al., 2016).

http://alien.jrc.ec.europa.eu/SpeciesMapper


33

invasive alien species, this could be a useful spatial predictor of indigenous species level biodiversity. However,

only 13 of the EU Member States have been able to provide data to supplement the EASIN 10km grid data,

resulting in some Member States where data is sparse (see Tsiamis et al., 2017).

Biophysical environmental data for informing a spatial modelling approach should also be supplemented with

more detailed information on environmental pressures and anthropogenic activities. For example, investment in

improved spatial datasets on land use (beyond the Land Use and Coverage Area frame Survey - LUCAS) is

likely to be highly relevant to explaining bird species-diversity (as Siriwardena et al. 2012 demonstrate for

cropland). As a proxy of anthropogenic impacts, using the accessibility of each grid cell would also be useful to

explore. Explicit incorporation of impacts such as chemical, light or noise pollution may also be valuable to

include.

Table 13: Bird monitoring schemes considered for use in integrated assessment of the effect of agricultural land

use on farmland birds (from Pringle et al., 2017, also available at https://pecbms.info/country/)

MS Coverage Years Total number of

sites across all

habitats and years

Site selection

AT National 1998- 435 free choice

BE Brussels 1992- 114 other

BE Wallonia 1990- 4199 unknown

BG National 2005- 166 stratified random

CY National 2006- 158 unknown

CZ National 1982- 331 free choice

DK National 1976- 1758 free choice

EE National 1983- 181 free choice

FI National 1975- 1436 free choice

FR * National 2001- 2541 unknown

FR * National 1989-2001 Unknown unknown

DE * National 1989-2010 Unknown Free choice

DE National 2005- 1436 stratified random

GR National 2006- 110 stratified random

HU * National 1999- 441 stratified random

IE National 1998- 401 stratified random

IT National 2000- 958 random

LV * National 1995-2006 Unknown random

LV National 2005- 97 random

LI National 1994- 127 stratified semi-random

LU * National 2009- Unknown stratified random

NL National 1984- 7402 free choice

PL National 2000- 1005 stratified random

PT National 2004- 1029 stratified random

RO National 2007- 203 semi-random

SK National 1994- 124 free choice

SI National 2007- 119 stratified non-random

ES National 1998- 1239 stratified random

https://pecbms.info/country/


34

ES Catalonia 2002- 543 stratified

SE National 1975- 1169 free choice

SE National 1998- 716 systematic

UK National 1966- 5327 stratified random

* indicates specific datasets were omitted from the Pringle et al., (2017) analysis owing to the timing of data

availability or the spatial resolution available

Fundamentally, the generation of an EU wide dataset of survey data to inform any modelling approach will

require engagement with the national monitoring programmes of all EU Member States, to create species counts

for each sampling location on the core 1km accounting grid. Across Member States there are differences in

sampling strategies that will need to be accounted for to make the dataset comparable across monitoring

programmes. Achieving this consistency will require input from monitoring experts and quantitative ecologists,

and involve dealing for variation in sampling effort, sampling design (e.g. stratified random sample versus free-

choice, as shown in Table 13) and sampling methodology (e.g. birds in flight versus birds in the nest or on the

ground). Understanding what the key limitations in these regards will also inform on wider modelling methods.

For example, considering whether extrapolation should be performed within Member States based on models

fitted for that State or should be based on an EU wide model (or a hybrid approach).

3.3.3. Other Spatial Datasets that can be integrated into the Roadmap

In section 3.3.2 a range of datasets was introduced that could support modelling approaches under the Roadmap

to move to an EU scale full spatial approach for common bird Species Accounting. Here other spatial datasets

are considered relevant to Europe that could be used to increase the taxonomic coverage of the accounting. The

prime EU datasets considered in these regards are those reviewed in Appendix C, which benefit from geo-

referenced data on species. These are summarised in Table 14. The final column of Table 14 provides an

assessment of the possibility of the dataset to be integrated into a regularly compiled spatial biodiversity account.

Only those considered to have a ‘medium’ or higher possibility are included further in the following

discussion.10

Table 14: Potential European Spatial Species Datasets for Integrated Biodiversity Accounting

Dataset Steps required to

integrate with the

1km accounting

grid

Extent of

coverage

Spatial

Resolution

Temporal

resolution

Source Possibility to

support regular

accounting

Atlas of

European

Mammals

Downscaling

required - Need to

find input data

layers

Pan-

Europe

Unclear -

Range and

distribution

maps

Pre and post

1970 - limited

use

https://www.eur

opean-

mammals.org/p

hp/mapmaker.p

hp

Low due to spatial

and temporal

resolution

European Red

List of Bees

Downscaling

required - Need to

find input data

layers

EU27 and

Pan-

Europe

Unclear -

Range and

distribution

maps

Every 10 years http://ec.europa.

eu/environment/

nature/conservat

ion/species/redli

st/downloads/Eu

ropean_bees.pdf

Low due to spatial

and temporal

resolution

European Red

List of

Butterflies

Downscaling

required - Need to

find input data

layers

EU27 and

Pan-

Europe

Unclear -

Range and

distribution

maps

Every 10 years http://ec.europa.

eu/environment/

nature/conservat

ion/species/redli

st/downloads/Eu

ropean_butterfli

es.pdf

Low due to spatial

and temporal

resolution

10 With respect to the ‘Atlas Hymenoptera’, the JRC have developed a working approach for integrating this data with the

1km accounting grid and this dataset is not considered further in the discussion.

https://www.european-mammals.org/php/mapmaker.php





http://ec.europa.eu/environment/nature/conservation/species/redlist/downloads/European_bees.pdf






http://ec.europa.eu/environment/nature/conservation/species/redlist/downloads/European_butterflies.pdf








35

Atlas

Hymenop-tera

Modelling required

(see below). Expert

based model used

(100 x 100 m) and

species distribution

model (10 x 10 km).

SDM is constrained

by spatial and

temporal resolution

of the species

records

Belgium,

France,

Europe,

Africa,

Turkey

See below

(Vallecillo

et al., 2018)

See below

(Vallecillo et

al., 2018)

http://www.atlas

hymenoptera.ne

t/

See below

(Vallecillo et al.,

2018)

Vallecillo et

al. (2018)

Ecosystem

services

accounting :

Part I -

Outdoor

recreation and

crop

pollination

Provides an

approach for

downscaling Atlas

Hymenoptera and

other pollinator data

using CLC

EU28 1km grid

via JRC

method

Can be based on

CLC editions

http://publicatio

ns.jrc.ec.europa.

eu/repository/ha

ndle/JRC11032

1

High but

methodological

approach requires

validation

LepiDiv

Project –

European

Butterflies

Downscaling

required - Need to

find input data

layers

EU28 average

transect

length =

1270 m

Unstructured set

of temporally

referenced

observations

http://www.ufz.

de/european-

butterflies/index

.php?de=42605

Low due to

coverage - only

data on

Luxembourg and

Italy available at

current time

GBIF Data needs to be

extracted for

relevant years and

procedures

developed to correct

for spatial, variation

in sampling effort.

Modelling

approaches could

then be employed to

extrapolated tis data

(see left tool as

example)

Europe Point data Unstructured set

of temporally

referenced

observations

https://www.gbi

f.org/the-gbif-

network

Medium

European

Alien Species

Information

Network -

EASIN

Need to extract data

from EASIN.

Species catalogue

and species

geodatabase (10 x

10km grid)

EU 28* points or

grid

10kx10km

from 2016

(reported

continuously)

https://easin.jrc.

ec.europa.eu/eas

in

Medium

EU STEP

project

Project is ongoing -

data would need to

be extracted. Recent

publications based

on the data have

used species

distribution

modelling, together

with climate and

land use data

EU 28 Proposal 5 x

5 km

To be confirmed http://www.step

-

project.net/?P=2

0

Low at present -

project may

generate outputs

to support regular

accounting in the

future

http://www.atlashymenoptera.net/



http://publications.jrc.ec.europa.eu/repository/handle/JRC110321





http://www.ufz.de/european-butterflies/index.php?de=42605




https://www.gbif.org/the-gbif-network



https://easin.jrc.ec.europa.eu/easin



http://www.step-project.net/?P=20





36

European

Butterfly

Monitoring

Scheme

(eBMS)

Would need to be

collated from

different countries.

Likely to require

harmonisation of

different sampling

methods

11 EEA

countries

Transect

data

(approx.

54,000)

Yearly http://www.bc-

europe.eu/index.

php?id=339

Medium – High

Service

contract in

relation to

target 3A –

Agriculture

Provides a

metadabase of

biodiveristy data

holdings for the EU

EU 28 Metadatabas

e

Various https://publicati

ons.europa.eu/e

n/publication-

detail/-

/publication/cd1

c6a81-969e-

11e7-b92d-

01aa75ed71a1/l

anguage-en

Needs further

exploration - key

datasets for the

EU captured in

this table

European

Vegetation

Survey

Procedures required

to correct for spatial

variation in

sampling effort.

Modelling

approaches could

then be employed to

extrapolate this data

Europe

(continent

al scale)

Plot data

(>4.3

million

records)

Unstructured set

of temporally

referenced

observations

http://euroveg.o

rg/

Medium - requires

significant

investment

Pollinators are recognised as being of great importance to agricultural production, whilst their conservation

status is of concern. As a result they are a group for which monitoring is increasing. However, there still remain

a number of EU countries without national monitoring programmes, and these monitoring programmes are far

less comprehensive (both sampling density and time series) than the bird monitoring surveys established in

almost all EU countries. There are several EU research projects focussing on the biodiversity of pollinators, for

example the EU STEP – Status and Trends of European Pollinators (STEP, no date). However, the most

analogous data to that of the PECBMS are butterfly monitoring schemes. These are present in many EU member

states (see BCE, no date), and global guidance on standardised schemes are available (van Swaay et al., 2015).

Combining this data to a European dataset could enable spatial extrapolation of butterfly biodiversity variables to

the EU-scale. However, observational data appears to be less spatially extensive than for birds, and this is likely

to limit the feasibility of any extrapolation.

3.3.4. Next Steps for the Roadmap

Table 15 outlines the steps on the roadmap regarding modelling and data to generate EU-wide spatial bird and

other species accounts (i.e., a spatially integrated biodiversity account). It should be noted that implementing the

roadmap will require investment to organise the required data foundation across the various bird monitoring

programmes. It will also require development of an ecologically suitable methodological model, and accounting

approach at the EU scale. However, the data is already in place, and institutional arrangements are established

between national monitoring programmes and the EBCC. This may help to minimise transaction costs for

implementing the roadmap. With appropriate financial commitments and methodological development, these

institutions are likely to be receptive to participating in the process, and contributing the delivery of the roadmap

in support of regular ecosystem accounting and wider environmental decision making. This will be especially

relevant, for example, following release of CLC editions. These institutions will be well aquatinted with

modelling habitat and spatial relationships with these data. This would enable the lessons learned from existing

approaches to be adapted to the accounting context, rather than in re-testing methodologies and re-analysing

these data from scratch.

Table 15: Roadmap steps to generate an EU spatial Integrated Biodiversity Accounts

http://www.bc-europe.eu/index.php?id=339



https://publications.europa.eu/en/publication-detail/-/publication/cd1c6a81-969e-11e7-b92d-01aa75ed71a1/language-en









http://euroveg.org/

http://euroveg.org/


37

Time-scale Modelling Issues Data Issues

First-steps:

possible

within 3

years

Evaluate current methodology on other BBS

years and Czech data

Establish institutional arrangements on data sharing

with data holders

Incorporate variable detectability into models Work with stakeholders (particularly the national bird

monitoring programmes) to account for different

sampling approaches

Build consensus on response variable(s) (e.g.,

species richness, Shannon index or relative

abundance) and the best modelling approach for

spatial extrapolation at the species or

community level. Explore hierarchical mixture

modelling of site occupancy and abundance

Process country data, to get a harmonised data set at

1km

Explore how the bird species should be grouped (e.g.,

just common birds, by habitat specialists or into

different groups with different functional traits

Agreement on predictor variables and EU wide data

sources (e.g., CLC, LAI, Population, Climate other

pressures) and collect data at the appropriate resolution

Next-steps,

3 to 5 year

horizon

Explore whether country specific models, or

globally generated models perform best for

spatial extrapolation

Explore independent data-sets that can be used to

evaluate the spatially extrapolated data

Use comparative analyses to identify where

regional modelling is needed to extrapolate

across countries, or areas where training data is

unavailable

Begin integration of other species groups into the

analysis

3.4. Considering ecological risks in the context of SEEA Natural capital accounting is increasingly seen to provide an essential information framework that can inform

integrated management and policy responses and help deliver on the targets set out in the 7th EAP and EU

Biodiversity Strategy. However, the ‘stock and flow’ model of ecosystems and their services masks the

considerable complexity of ecosystem dynamics. Loss of ecosystem condition may be characterised by

thresholds, nonlinearities and irreversibility with respect to the continued delivery of ecosystem services.

Furthermore, in many cases there remains considerable uncertainty with respect to the functional relationships

that exist between and ecosystem condition metrics and levels ecosystem service delivery. This is particularly

the case for biodiversity (Harrison et al., 2014).

For the UK, the Natural Capital Committee have proposed the use of risk registers for identifying ecosystem

assets, where further loss of condition places ecosystem service delivery at risk (Mace et al., 2015). This section

presents an experimental application of this approach, drawing on the integrated biodiversity account for the EU

presented in Section 3.2. A fuller description of the use of risk registers in the context of natural capital

accounting is provided in (IEEP et al., 2017), submitted as a final deliverable in the first year of the KIP-INCA

support contract.

3.4.1. Risk Register Approach

The use of a risk register fits within the current experimental context of ecosystem accounting, and is considered

complementary. Risk registers allow an integration of information on ecosystem extent and condition, with

information on the potential implications for ecosystem service delivery. They also allow for risks to be

considered where there are gaps in knowledge, for instance with respect to quantifying thresholds and functional

relationships in the ecosystem asset-service relationships. eftec and Cascade Consulting (2013) provide a

practical application of the approach. Their process was based on expert judgement, with reference to the UK

National Ecosystem Assessment, and resulted in 73 priority ecosystem asset-service benefit relationships being


38

identified. A number of approaches were used in order to set targets for these asset-service relationships. This

included the use of policy targets where available and the use of suitable proxies where such targets were

unavailable (e.g. the condition of sites of special scientific interest for similar habitat categories), to inform on

the status of the ecosystem service benefit. In addition, available information on the trends in the extent,

condition and spatial configurations of each habitat type with respect to each asset-service relationship, was

assessed to ascertain the likely future status of the asset and potential benefits flows. Based on the assessment of

these targets for benefits and trends in assets a red, amber or green risk rating was attributed to the asset-benefit

relationship using the risk scoring matrix in Figure 7.

For the test case risk register for the EU, the functional relationships between the quantity and condition of the

ecosystem asset, and the delivery of selected ecosystem service benefits are assessed.11 For developing this test

case, the following stepwise approach is adopted:

1. Drawing on available literature: Identifying the ecosystem services most relevant to the

characteristics (or functional traits) of the species groups considered in the integrated biodiversity

account. This draws on the Luck et al., (2009) proposal that a collection of individuals from a given

species with certain characteristics, are necessary to deliver certain ecosystem services.

2. Drawing on key EU environmental policies: Identify policy target levels for species that represent a

suitable proxy for the socially satisfactory level of delivery of these ecosystem services.

3. Drawing on the integrated biodiversity account (Table 11): For each MAES type, using trends in

ecosystem extent, to identify where land conversion represents a risk to maintaining the quantity of

these types of ecosystem assets (one measure of the ‘Stock’ of natural capital). This is used to infer

where habitat loss may represent a risk to the future delivery of these ecosystem services (although it is

acknowledged that MAES typologies may be too broad to inform on this).

4. Drawing on the integrated biodiversity account (Table 11): For each MAES type, using trends in

relevant species condition metrics to assess risk to the quality of these types of ecosystem assets (the

second measure of the ‘Stock’ of natural capital). This is used to infer where a loss of ecosystem

condition for biodiversity may represent a risk to the future delivery of these ecosystem services.

Based on the outcomes for point 1, three species groups from the integrated biodiversity account are considered

in the risk register. These are: Birds; Butterflies; and, Fish (derived from the ecological status assessment of this

species group under WFD reporting).The relevant ecosystem services and policy target levels for each species

group are summarised below.

3.4.1.1 Birds

Wild bird populations deliver a range of ecosystem services. These include as a source of wild food resources,

recreational services associated with bird watching and hunting of game species. However, given their

widespread nature, delivery of regulating ecosystem services may be the most important with respect to common

bird species in the EU. In this regard, studies in both natural and agricultural habitats show that birds reduce

herbivorous insect populations and that plants also respond with higher growth rates or crop yields (Wenny et

al., 2011). Furthermore, nearly a third of bird species disperse seeds, primarily through fruit consumption, but

also through scatter-hoarding of nuts and conifer seed crops. This includes seed dispersal of many species with

direct value to humans for timber, medicine, food, or other uses. Consequently, pest control and seed dispersal

are identified as key ecosystem services with respect to birds.

Target 1 (ii) of the EU 2020 Biodiversity strategy provides a policy target of 50% more bird assessments under

the Birds Directive showing a secure or improved status (EC, 2011).The European Red List of Birds Consortium

(2014) used the 2012 Article 12 reporting data to establish the baseline for this target. The results of this study

11 The spatial configuration of ecosystem assets within the landscape is also a critical factor in the delivery of ecosystem

services. As the integrated biodiversity account adopts a minimum spatial approach this is not considered in the test case risk

register. However, it could be incorporated based on outcomes from implementing the methodological roadmap presented in

section 3.3.


39

were used in UNEP-WCMC (2017) to create a species status based account for birds at the EU scale. An

aggregated Index is calculated from these data for each MAES ecosystem type.12 This is based on the Red List

Index approach and represents the average distance from a target of all birds being ‘Secure’, where the index =

100. Inspection of Equation 1 in UNEP-WCMC (2017), reveals that as there are only 3 status levels, reducing

the distance between the current level of the aggregate indicator, and 100 by 50% is analogous to achieving

Target 1 (ii) of the Biodiversity Strategy for that ecosystem type. It should be noted that a repeat study following

the European Red List of Birds Consortium (2014) approach is required using updated Article 12 reporting data

to calculate metrics endorsed by the European Commission to measure progress towards Target 1. This would

then inform the calculation of an updated aggregated Index by MAES ecosystem type, as described in UNEP-

WCMC (2017).

3.4.1.2 Butterflies

As highlighted by the (EC, 2018) whilst recent public focused on bee populations, a dramatic decline in the

abundance of all kinds of European wild insect pollinators, including butterflies occurred. Furthermore, as

Thomas (2005) observes, butterflies are also good (indirect) indicators of insects and generally insect pollinators.

Therefore, the trend in grassland butterflies is thus an indicator for the health of grassland ecosystems and their

capacity to deliver pollination services. Although realising these services in arable agricultural ecosystems

require proximity to semi-natural grassland ecosystems, which may not often be reflected in existing land-use

patterns.

Target 1 (i) of the EU 2020 Biodiversity Strategy is for 50% more species assessments under the Habitats

Directive, to show an improved conservation status. Given grassland butterflies (and butterflies more generally)

which are listed in the annex of the Habitats Directive,13 it is considered appropriate to use the Target 1 (i) goal

as a policy proxy target level for a sufficient delivery of insect pollination services from grasslands in the risk

register. The EEA (2013) identifies that the Grassland butterfly index has declined by 30% by 2011 (well aligned

to the time Target 1(i) was proposed). As such, achieving the policy goal for butterflies under Target 1 (i)

requires an increase in the indicator back to 85 (100 – (30/2)).

3.4.1.3 Fish

Holmlund & Hammer (1999) review a large number of ecosystem services provided by fish. These include the

Common International Classification of Ecosystem Services (CICES) regulating services, such as maintaining

the chemical condition of freshwaters (by nutrient cycling), or regulation of chemical composition of the

atmosphere (by regulation of carbon fluxes from water to atmosphere). However, the two most tangible services

identified by Holmlund & Hammer (1999), are for food provisioning services and cultural recreational services

associated to river and lake fishing. These are selected for inclusion in the risk register.

With respect to identifying a policy target for a socially desirable level of fish related ecosystem service delivery,

the WFD sets out a target of achieving 100% GES for all water bodies (EC, no date). This may be offset, to a

degree, by achieving GES for other biological quality elements. However, the 100% GES target is still

considered an appropriate proxy for a satisfactory level of fishing recreational and provisioning ecosystem

services in the risk register.

3.4.2. Test Case Risk Register

Based on the assessment of the distance from policy target levels, and the trends in ecosystem extent and species

condition presented in the integrated biodiversity account (Table 11), a red, amber or green risk rating was

created for each ecosystem service benefit and asset relationship. This risk rating is attributed to each relevant

MAES type, for each ecosystem service using the scoring matrix in Figure 7.

12 This is the Article 12 Birds Directive (All birds Aggregate Status Index) in Table 11 13 Phengaris nausithous; Phengaris arion; and, Euphydryas aurinia are listed in the Grassland Butterfly Indicator and the

Habitats and Species Directive. Overall 29 species of butterfly are listed on the Annexes of the Habitats and Species

Directive (van Swaay et al., 2010).


40

Figure 7: Risk rating matrix

The risk rating score is based on the following:

1. Status of ecosystem service benefit: The status of the ecosystem service benefit reflects the socially

satisfactory level of delivery for the service. For the NCC application in England, policy target levels

are used to represent a suitable proxy (as previously described with respect to eftec and Cascade

Consulting, 2013). Target 1 (ii) of the EU Biodiversity Strategy calls for a 50% increase in birds

showing a secure or improved status. Using this as a proxy for the status of pest control and seed

dispersal services by birds, implies the status of these ecosystem services is ‘Below Target’ (albeit on

the boundary with ‘Substantially below target’). Similarly, relating the grassland butterfly score to

Target (i) of the Biodiversity Strategy and using this as a proxy for insect pollination services the status

of this service is ‘Below target’. As only 47% of water bodies are of GES for fish species, the status of

recreation fishing and fish provisioning services is ‘Substantially below target’.

2. Trend in ecosystem asset (extent): Ecosystem extent at the MAES level is relatively stable (all

changes <0.25% between 2006 and 2012 in Table 11). As such no positive or discernible trend in this

aspect of the stock of MAES ecosystem assets is identified.

3. Trend in ecosystem asset (species condition): A trend in species condition is considered to be strongly

negative when it is in excess of 10% in Table 11. However, this is acknowledged as a somewhat

arbitrary value.

Figure 8, presents the test case risk register based on risk ratings detailed above. In Figure 8, Ext. represents

‘extent’ and Species Con. represents ‘species condition’ (as described above). Given that Urban and Marine Inlet

ecosystems are unlikely to contribute significantly to pest control or seed dispersal services associated with

birds, these are not considered in the risk register. Grey cells indicate there is insufficient information to assign a

risk rating to the respective ecosystem service for that MAES ecosystem type. The risks to ecosystem services

delivery associated with spatial configuration (Sp. Config in Figure 8) are not considered, as the risk register is

based on data from the minimum spatial approach to creating an integrated biodiversity account. This could be

incorporated if a fully spatial approach could be achieved.


41

Figure 8: Test case risk register for the EU. Grey cells indicate insufficient data to assess risk

3.4.3. Policy Insights from the Test Case Risk Register

The EU’s 7th EAP calls for conserving and enhancing natural capital, which is also reflected in maintenance and

restoration of ecosystems and their services under Target 2 of the Biodiversity Strategy. The risk register

provides a useful tool for evaluating which ecosystems, and which characteristics of those ecosystems should be

the focus of such enhancement or conservation actions. It also allows trade-offs to be explored in the context of

available environmental budgets for different restoration options, and their potential benefits. Potentially, the risk

register approach could also be developed into an ecosystem account, by tracking how many risks increase or

decrease in certain ecosystem asset types over time.

3.4.3.1 Combined presentations to mainstream natural capital into sector planning

One of the principal ways of integrating information form ecosystem accounts with the standard system of

national accounts, is through the use of combined presentations (See Chapter 8, SEEA EEA TR, UN et al.,

2018). The risk register provides a summary of the information on ecosystem extent, condition and services.

With respect to the delivery of pest control services from birds, the trend in condition identified for these species

in cropland and grassland (including pastoral systems) reveals a high risk to this ecosystem service benefit. In

terms of agricultural production, the loss of this service could have significant impacts. For instance, Mols and

Visser (2002, in Whelan et al. 2008) reported that emplacement of nesting boxes to attract great tits (Parus

major, a common bird species), reduced caterpillars and fruit damage and increased fruit yield. The reported

increase in yield was striking, from 4.7 to 7.8 kg apples per tree (66%).This implies the relatively low supply of

this ecosystem service was having substantial economic impacts, which should be considered in sector planning.

Combined presentations allow for these risks to be put in context and communicated to planners. For this

scenario, statistics from Eurostat (2016) for the fruit and vegetable sector reveal the value of the EU’s output of

dessert apples alone, at basic prices, was in excess of €4 billion in 2014. In combination, this information

provides an economic argument for investing in improving bird populations in in fruit producing areas.

However, further evaluation of wither there is widespread evidence of this service being realised in temperate

systems is required, as well as an assessment of the costs associated with controlling pests by other

(environmentally sustainable) means.

Combining data from different elements of the national accounts, also has the benefit of providing a coherent

picture based on official statistical sources. However, many sector planners and wider decision makers will use a

variety of information sources to inform their decision making processes. With respect to the delivery of insect

pollination services from grassland, the EU pollinator initiative identifies that up to almost €15 billion of the

EU’s annual agricultural output is directly attributed to insect pollinators (EC, 2018). Although the risk to the

continued delivery of this service indirectly suggested by the decline in the grassland butterfly index, it is clearly

communicated via publications such as the EEA (2013)14. The risk register provides a tool to present this

alongside other information on ecosystem services, to identify where synergies and conflicts may arise. For

instance, increasing the population of insectivorous birds could be at odds with improving insect pollinator

populations.

14 The term ‘indirectly’ reflects that butterflies may not play a significant role in crop pollination themselves but do represent

a proxy for the status of wider insect pollinators in grasslands. It is also noted that grasslands will need to be located in

proximity to croplands for this service to be realised.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Ext.

Spec

ies

Co

n.

Sp.

Co

nfi

g.

Pest control

Seed dispersal

Insect

Pollination

Food

provisioning

Recreation

Cropland Grassland Woodland / ForestSparsely Vegetated WetlandsRivers / LakesHeathland / Shrub


42

Maintaining the flow of intermediate ecosystem services is also essential for maintaining the resilience of

ecosystems across all scales. For example, seed dispersal by birds and insect pollination will also support a

diversity of wild plants in various locations and facilitate adaptation and phenological shifts in the advent of

shifting condition, including due to climate change. In turn these services will be important for maintaining

viable populations of wild crop relatives. This is an important provisioning service (see Haines-Young and

Potschin, 2018), the true value of will only become apparent over time. As such, the risk register can support

precautionary decision making to protect these services by communicating risks in bio-physical terms.

3.4.3.2 Maximising social welfare

In many policy scenarios, market prices may provide too narrow a concept of value to inform decision making in

the public interest. This is often the case in environmental planning decisions, where societies often receive

many ecosystem service benefits that accrue outside of well-functioning markets. In these scenarios, the

decision-maker is in charge of maximising social welfare and requires information on welfare values for

decision-making. The risk register can also be presented in combination with these types of values. For example,

the risk register identifies a medium risk to recreational services associated with river and lake fishing and

associated provisioning services. The social welfare values associated with recreation fishing, and inland

fisheries more generally, can be quite large. The Environment Agency (2009) show this for England and Wales,

via a Choice Experiment administered to members of the general public to estimate their willingness-to-pay

(WTP) to protect salmon stocks for recreational fishing and other purposes. They identified a WTP of £15.80

amongst participants, which equates to £350 million if aggregated to the population of England and Wales.

In consideration of the above, the risk register could also provide a platform for exploring the possible trade-offs

in social welfare, versus economic sector related benefits, and where synergies or conflicts arise. In this context,

there could be significant co-benefits in improving pollination and seed dispersal services, as they are important

for maintaining wild flowers and plants. This will provide cultural ecosystem services tied to experiencing high

value natural environments.

3.4.4. Options for Development of the Test Case Risk Register

It should be noted that the risk register presented in Figure 8 is a test case to illustrate the possible applications of

this tool in an ecosystem accounting context. It has been rapidly developed using available policy targets and

information as an example application. Key next steps for developing the tool to support the mainstreaming of

natural capital into EU economic and land use planning processes include:

● Move to a spatial biodiversity accounting approach, so that risks associated with the spatial

configuration of ecosystem assets can be captured in the risk register. The first step in this regard should

be implementing the roadmap proposed in Section 3.3.

● Develop more reliable accounting inputs that can feed into the register (e.g., integrating the EEA work

to achieve more consistent Water Quality Accounts that address the issue of inconsistent sampling

between WFD reporting).

● Update accounting inputs following the forthcoming release of Corine Land Cover Maps, and provision

of new reporting data from the EU nature directives.

● Expand the basket of ecosystem services considered. The tool provides an opportunity to consider wider

ecosystem services, for which ecosystem service accounts cannot be compiled (i.e. due to resource, data

or ecological knowledge constraints). As such it provides a useful communication tool to decision

makers.

● Refine the links between species condition parameters and species related ecosystem services. For

example, bird species could be grouped according to functional traits relevant to ecosystem services.

These may provide a more definitive link between stocks of certain species recorded in the biodiversity

account, and ecosystem services.


43

● Seek expert opinion to quantify these target levels specifically, rather than relying on policy targets that

are, clearly, designed for other purposes.

3.5. Conclusions The integrated biodiversity account compiled presented in Table 11 reveals the trends in different species metrics

are generally consistent by MAES ecosystem type at the EU level. This provides some confidence that the

Article 12 based approach produced in 2017 is working well at the supranational scale, even if issues emerge at

the Member State scale.

However, of the readily available species based data items tested in Table 11, there are very few that are not bird

based. Essentially the grassland butterfly indicators and the WFD reporting data are the only non-bird statistics

readily available for direct integration of an EU scale biodiversity account, by MAES ecosystem type. Therefore,

further investment is clearly required in developing a wider range of indicators aligned with the MAES typology.

Improving current processing efforts (e.g., via the ongoing work at the EEA with respect to Article 17 and the

WFD data) will be helpful in these regards. However, investment is clearly needed to improve or establish

monitoring programmes across Member States and the EU, to broaden the taxonomic coverage of indicators for

species status and species-level diversity.

The exploratory modelling described in Section 3.3.1 was encouraging for the idea of using modelling

techniques to spatially extrapolate bird species biodiversity data. A key finding in the exploratory modelling was

that relying on CLC Level 3 cover provided more explanatory power than using the broader MAES typologies.

However, implementing a roadmap for moving to fully spatial approaches at the EU scale, will require much

greater depth of exploration. This will also require combining a more extensive set of bird and environmental

datasets, and a more rigorous assessment of methodologies. However, bird monitoring programmes are well

established throughout Europe, and institutional arrangements are in place between these programmes and

organisations such as the EBCC. Clearly realising the potential to compile Species Accounts using this data on a

regular and ecologically meaningful basis, will require a joint effort between ornithologists and ecosystem

accounts. Other spatial datasets could be included into such a spatial biodiversity account, with possibilities for

butterflies identified. The roadmap provides a useful structure to support discussions in these regards.

A key objective of ecosystem accounting is to integrate information in a consistent framework, using ecosystem

assets as the conceptual unit for such integration. As such, the insights that can be provided by biodiversity

accounts are quite limited when presented in isolation. The risk register is found to provide a useful way to

provide a qualitative overview of the links between different ecosystem accounts. The test case using the

integrated biodiversity account identifies where some risks may exist, but also where some synergies and trade-

offs emerge. The tool also provides an opportunity to consider wider ecosystem services, for which ecosystem

service accounts cannot be compiled. It also indicates where knowledge on the functional relationships between

stocks (extent and condition), and service flows is limited. The use of combined presentations drawing on the

risk register are shown to provide useful context to economic sector, environmental and social planners.


44

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48

Appendix A: Czech Republic Species

Accounts for Repeatedly Monitored

Locations A review of the derived JPSP dataset revealed that the number of transects was increasing significantly through

time (50 for 2000, 121 for 2006 and 143 for 2012). To enable comparison of the accounts between the three

periods, a dataset was created containing only point data for those transects that were sampled consistently in all

three periods. This left 35 transects and 700 sample points.

Tables A1 and A2 present the Common Bird Species Accounts for the repeatedly monitored locations for the

2000, 2006 and 2012 datasets. Table A1 reveals generally increasing species richness across MAES ecosystems

between 2000 and 2006, with the exception of Woodland / Forest (albeit species richness only decreases by 1).

An increase in the Shannon index is noted in Rivers and lakes ecosystems (+0.21) and a slight increase is noted

in Urban (0.08) and a slight decrease in Grassland (-0.06) between 2000 and 2006. A substantial increase in bird

populations across all ecosystems (around 40 million) is observed, mainly driven by increases in common bird

abundance in Cropland.

Table A1: Czech Republic Common Bird Species Account (Repeatedly Monitored Locations, 2000 - 2006)


49

Table A2: Czech Republic Common Bird Species Account (Repeatedly Monitored Locations, 2006 - 2012)

Table A2 reveals a reduction in species richness between 2006 and 2012 in Urban (-2) and Woodland / Forest (-

4) ecosystems. These are associated with reductions in the Shannon index (-0.10 and -0.07, respectively). The

Shannon index also reduces in River / Lake ecosystems (-0.07). A substantial reduction in common bird

abundance is observed across all ecosystems (approximately -15 million), again driven by changes in cropland

(approximately -12 million).

experimental species accounts for the eu · 2019. 3. 21. · type in the first period, and...

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