Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Developing UK indicators for the Strategic Plan for Biodiversity 20112020: Habitat Connectivity Indicator Options Paper
Indicator options:
Specialists in habitat connectivity identified three indicator options relevant to the UK’s obligation to
report on Aichi Targets 5 and 11 of the Convention for Biological Diversity’s Strategic Plan for Biodiversity
2011-2020, as well as other EU and national commitments:
A. Species-based indicator of habitat connectivity – based on changes in species distributions. No
methods suitable for immediate implementation have been identified. Potential methods might
be developed within five years, subject to funding of appropriate research.
B. Indicator of structural connectivity – based on simple structural metrics or a structural version of
the existing UK Biodiversity Indicator of habitat connectivity applied to the National Forest
Inventory and, therefore, focused solely on woodland; it could be deployed immediately.
C. Indicator of functional connectivity – based on the existing UK Biodiversity Indicator of habitat
connectivity applied to an indicator-specific land-cover map, which could be produced within one
year. The indicator could be presented for nested habitats, with a breakdown of values for its
structural and functional elements.
Data issues:
 Any measure of habitat connectivity (species-based, structural or functional) will only be as good
as the land-cover data on which it is based.
o Countryside Survey sample square data and Land Cover Map have limitations. They are
only updated every c.10 years. The grain of most landscapes is greater than Countryside
Survey 1km sample squares and there is evidence that some species respond to
connectivity at a landscape-scale. The degree of accuracy and inter-comparability of
succeeding editions of Land Cover Map hamper detection of changes in connectivity.
o The National Forest Inventory is the only suitable land cover dataset currently
maintained that would allow annual measurement of changes in connectivity.
o Indicator-specific land-cover maps could be produced annually from SPOT scenes and
Landsat (or GMES) data
 Change in the number or area of landscape-scale initiatives as a proxy for changes in connectivity
is not a feasible option due to lack of a standard definition and monitoring.
Other key background issues:
 Habitat fragmentation has a direct impact on the area, isolation and edges of habitat patches. The
characteristics of the intervening matrix surrounding habitat patches are also increasingly
recognised as having a strong influence on connectivity.
 In the UK, the dominant pattern of habitat loss is already established and the primary impacts of
fragmentation on surviving habitat patches are determined by changes in the intervening matrix.
 It would be beneficial to consider connectivity in relation to semi-natural habitats as a whole, as
well as to selected individual habitats.
 The most obvious measure of habitat connectivity may be to base it on changes in species
distributions but this approach is fraught with challenges. At a local scale, they may reflect other
drivers of change and over short timescales may be due to stochastic events, while at a regional
or national level and over longer timescales, they may be driven by changes in climate rather than
habitat connectivity.
 Structural measures of connectivity may be useful in detecting gross changes where there is
substantial ongoing habitat loss. However, they do not take into account the nature of the matrix
and some structural measures can be misleading, e.g. nearest neighbour distances decrease as a
result of habitat creation and fragmentation.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Other key background issues (continued):



The existing UK Biodiversity Indicator of habitat connectivity is an indicator of functional
connectivity that considers the area, isolation and edges of habitat patches as well as the
characteristics of the intervening matrix.
The existing indicator may be more acceptable to a wider constituency if:
o It can be applied at a broader scale (i.e. >1km 2)
o It is deployed as a structural measure of connectivity by excluding elements demanding
expert opinion (relative permeability of different land covers and edge effects) or by
considering them separately
There is a need to provide supporting analyses to help explain changes in indicator values.
1. Rationale and Approach
An overview of the project and general approach are provided in Annex 1. The existing UK indicator of habitat
connectivity was developed by Forest Research in collaboration with the Centre for Ecology and Hydrology,
using Countryside Survey (CS) data (Watts et al. 2008; Watts & Handley 2010). It provides a significant step
forward in our ability to understand and describe habitat fragmentation and connectivity; however, it has been
viewed as too constrained in its application to 1km squares, too reliant on expert judgement and too complex.
The UK Biodiversity Indicators Steering Group (BISG) has, therefore, identified that there remains a need to
develop alternative options that address these issues or that replace the indicator. This options paper builds
on the outputs from a workshop attended by leading experts on habitat connectivity who hold a range of
views.
Specific objectives of the workshop and subsequent work were:
 To review sources of spatial land-cover data and rank them against those criteria for quality testing
indicator options that relate to data issues;
 To assess the option of creating a composite of existing spatial land-cover/land-use datasets;
 To evaluate options for developing a new indicator-specific spatial land-cover/land-use dataset;
 To consider pros and cons of species-based, structural or functional indicators of habitat connectivity
and rank them against the criteria for quality testing indicator options; and
 To identify three options for using/updating/developing an indicator of habitat connectivity.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
2. Summary of Data Sources
Any indicator of habitat connectivity (functional, structural or species-based) will only be as good as the landcover data on which it is based. Possible sources of land cover data are ranked against the criteria for quality
testing indicator options that relate to data issues (Annex 2). Indicator-specific land-cover maps produced
from SPOT scenes and Landsat (or GMES) data score consistently highly. They do not score maximum marks
only because past coverage relies on Landsat data, which is at a lower resolution than SPOT scenes or GMES
data.
Further details of issues that should be considered in relation to possible sources of land cover data are
provided below:
CS sample squares




Data security – dependent on Government priorities.
Data transparency – data not publicly available, only CEH could run the indicator and Forest Research
encountered problems getting GIS programmes to work on CEH computers.
Frequency of updates – only every c.10 years.
Geographic coverage – the current power analysis to determine the number of sample squares
required to provide a random stratified sample of the UK and each constituent country is not based
on needs in relation to measuring habitat connectivity, so may not be representative; the grain of
most landscapes is greater than 1km square; there is evidence that species respond to connectivity at
a landscape-scale (e.g. 5–10km square) not within a 1km square; the tight scale may partially explain
why significant change has not been detected by the current indicator; it only allows disaggregation
to a country level when regional analyses may be more informative.
Land Cover Map (LCM)



Time series availability – the methodology has changed each time a new map has been produced; an
ongoing review by CEH suggests that comparability between previous maps is likely to be very difficult
to achieve, although it may be possible at a wider scale or using broader land cover categories (e.g.
combining all grassland biotopes); CEH hope inter-comparability may be possible for future maps.
Data security – dependent on Government priorities.
Frequency of updates – only every c.10 years.
National Forest Inventory (NFI)



Time series availability – the first NFI was produced in 2010 and adopted a different methodology to
the previous National Inventory of Woodland and Trees with which it is not comparable; however, the
intent is to update the NFI on an ongoing basis, so there could be 9 years’ data by 2019.
Data security – dependent on Government priorities.
Geographic coverage – binary, only identifies woodland, would not allow consideration of the
intervening matrix; 0.5ha minimum mapping unit.
Indicator-specific land-cover maps produced from SPOT scenes and Landsat (or GMES)
data



Accuracy/precision – preferable to base attribution of land cover on what can be differentiated
rather than on an a priori classification.
Data security – SPOT scenes cost approximately £2,500 (although government agencies hold some
archive copies), Landsat data is free and will be replaced by GMES data post-2014. SPOT scenes and
Landsat data/GMES would need to be used in combination. Their utility depends on the ability to fund
attribution of land cover.
Data transparency – a baseline land-cover map could be attributed with reference to CS sample
squares (but see above) or using a combination of aerial surveys, existing field surveys and additional
phase 1 surveys, where necessary.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.


Frequency of updates – use of SPOT scene and Landsat (or GEMES) data would allow indicator values
to be calculated annually. Cloud cover may pose challenges to data-capture for some parts of the UK;
although where cloud cover is thin the GMES RADAR satellite mat will penetrate it.
Geographic coverage – to avoid the cost of producing a UK-wide map, a power analysis tailored to
measuring habitat connectivity could be undertaken to identify the minimum number of 60 x 60km
(3,600km2) SPOT scenes that would be representative of the UK, each constituent country, and
regions; Spot scene pixel size is 10m (SPOT 5) or 20m (SPOT 4) but Landsat minimum mapping unit is
0.5ha (as pixel size is 35m and 4-5 are required to attribute land cover); data from the GMES satellites
could augment or replace Landsat and SPOT data post-2014 at the same resolution as SPOT scenes.
Combined datasets




Different datasets have different purposes, spatial mapping standards, and recording intervals.
It may be possible to combine them at any one point in time but it is unlikely to be repeatable.
Combining datasets is likely to lead to detection of change through data incompatibilities rather than
real change.
It might be possible to establish a baseline map and then identify habitats restored/created (e.g. from
Environmental Stewardship - ES) or lost (e.g. using Ordnance Survey - OS data with regard to
development).
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
3. Indicator Options
Option A: Species-based indicator of habitat connectivity
The indicator (description and interpretation): The most obvious measure of habitat connectivity may be to
base it on changes in species distributions but this is fraught with challenges. Changes in species distribution
are not in themselves a surrogate for changes in habitat connectivity. At a local scale, they may reflect other
drivers of change and over short timescales may be due to stochastic events, while at a regional or national
level and over longer timescales, they may be driven by changes in climate rather than habitat connectivity. A
consequence is that there is an almost complete lack of empirical data on differential dispersal by different
species across different land covers (Catchpole, pers comm.) and evidence for the development of connectivity
to improve species movement is poor (Eycott et al. 2008, 2010, 2012). Four techniques for measuring habitat
connectivity have been considered that potentially overcome these challenges:




Mark-release-recapture;
Landscape genetics;
Population synchrony (i.e. correlations in abundance between sites over time) (Powney et al. 2011;
2012);
Changes in phenology vs. changes in species distributions (Amano pers comm.).
Further research on population synchrony or correlations between changes in species distribution and
phenology could lead to the development of an indicator within five years.
Data required: All of these methods are data hungry. Suitable existing data collection is only available for wellstudied taxa, e.g. birds and butterflies.
Strengths and weaknesses: Movement rates of individuals can be assessed by mark-release-recapture but this
technique is very time-consuming, expensive and misses rare and chance long-distance dispersal events (e.g.
Brommer and Fred 2007). Landscape genetics studies can be used if it is assumed that well-connected
populations have lower genetic diversity between populations (Storfer et al. 2007) but this is also a reflection
of how long they have been separated. Such techniques, although evolving rapidly, remain expensive and
time-consuming. Two key obstacles to further development and implementation of population synchrony or
changes in phenology vs. changes in species distributions as indicators of habitat connectivity are: time lags
between a landscape change and species responses; and the need to focus on species with intermediate
dispersal abilities (Watts et al. 2008, 2010; Watts & Handley 2010) rather than on the highly mobile species for
which suitable ongoing data collection is focused.
Cost of producing indicator1: The cost of sponsoring further research on population synchrony or correlations
between changes in species distribution and phenology could be substantial.
Example graphic: It is not currently possible to illustrate how a species-based indicator of habitat connectivity
might be portrayed graphically.
1
Costs are described as major (>£100,000); substantial (£50,000–100,000); significant (£10,000-£50,000); or minor (<£10,000).
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Option B: Indicator of structural connectivity
The indicator (description and interpretation): This indicator could be based on either:
1.
2.
A basket of simple structural metrics relating to number of patches (Figure 1), total area and core
area – fixed edge width (Figure 2), or
A structural version of the existing UK Biodiversity Indicator of habitat connectivity (Watts et al. 2008,
2010; Watts & Handley 2010). This would use a standard dispersal distance(s) and would not include
landscape permeability and edge values (Figure 3).
Data required: The ancient woodland inventories overlain with the National Forest Inventory.
Strengths and weaknesses: The indicator could be deployed immediately but would be focused solely on
woodland. It would not consider other habitats and would be insensitive to changes in the intervening matrix
other than those that relate directly to woodland cover. As such, specialists consulted suggest that obvious
changes in connectivity would go undetected.
1.
2.
Structural measures of connectivity can be misleading, e.g. nearest-neighbour distances decrease as a
result of habitat creation and fragmentation (Tischendorf & Fahrig 2000a; 2000b; Watts et al. 2008).
Specialists consulted advise that many simple indicators based solely on structural attributes of the
landscape are unlikely to be correlated with the population viability of species (Hodgson et al. 2009).
Previous constraints on the existing UK Biodiversity Indicator’s application to 1km squares are
addressed by this proposal. The existing indicator’s complexity is reduced and concerns about its
reliance on expert judgements are nullified, as landscape permeability and edge values would be
excluded from the model.
Cost of producing indicator:
Annual costs of applying a basket of simple structural metrics would minor.
Developing baseline land-cover maps from SPOT scenes and Landsat data may incur substantial costs.
Costs for detecting annual changes in land cover from SPOT scenes and Landsat data may be
significant. Annual costs associated with running the model to produce indicator values are likely to
be minor.
3.5
3
Number of patches
1.
2.
2.5
All woodland
2
Broadleaved, mixed and
yew woodland
1.5
1
Ancient semi-natural
woodland
0.5
0
2010
2013
2014
2015
2016
Year
Figure 1. Example graphic – Indicator of structural connectivity of UK habitats (number of patches)
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
3500
All woodland (total)
3000
Area (kha)
2500
All woodland (core)
2000
1500
Broadleaved, mixed and
yew woodland (total)
1000
Broadleaved, mixed and
yew woodland (core)
500
Ancient semi-natural
woodland (total)
0
2010
2013
2014
2015
2016
Year
Ancient semi-natural
woodland (core)
Figure 2. Example graphic – Indicator of structural connectivity of UK habitats (total and core area)
3.5
Mean connectivity value
3
2.5
All semi-natural habitats
2
1.5
Neutral grassland
1
Broadleaved, mixed and
yew woodland
0.5
0
2010
2013
2014
2015
2016
Year
Figure 3. Example graphic – Indicator of structural connectivity of UK habitats. Connectivity values are on a
scale of 0–100 with typical values for individual habitats being less than 1.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Option C: Indicator of functional connectivity
The indicator (description and interpretation): This option is based on the existing UK Biodiversity Indicator of
habitat connectivity (Watts et al. 2008, 2010; Watts & Handley 2010) (Figure 4). It attempts to reflect the
actual ability of species to move across landscapes.
Interpretation of the indicator would be aided by presentation of:





Nested habitats, e.g. semi-natural habitats as a whole, as well as those habitats currently addressed
by the indicator (broadleaved, mixed and yew woodland and neutral grassland);
Values for a structural version of the existing UK Biodiversity Indicator of habitat connectivity with
permeability and edge values as an add-on to consider if changes in the matrix may have been
significant in that particular year;
How changes in the indicator and its constituent parts reflects what has actually happened in relation
to policy delivery, e.g. the rate of habitat creation;
How changes in the indicator and its constituent parts relate to changes in species abundances and/or
distributions. However, experts consulted advise that, due to confounding variables and time lags in
species responses, evidence may be contradictory and will not reduce uncertainties;
Regional analyses to illustrate points, dependent on availability of data.
Data required: Production of the minimum number of indicator-specific land-cover maps from 60 x 60km
(3,600km2) SPOT scenes and Landsat data (or GMES data post-2014) that would be representative of habitat
connectivity in the UK, each country (and regions) – probably c. 10. Permeability values could be based on:



Delphi analyses of expert judgement (as currently)
Assessment of the similarity of habitats based on vegetation composition
A simple, transparent ranking of habitats that at an extreme might only distinguish between seminatural and intensive land use.
Strengths and weaknesses: Previous constraints on application of this indicator to 1km squares are addressed
by this proposal. The indicator-specific land-cover map could be created in 12 months, allowing deployment of
the indicator within two years. Experts consulted agree that the existing UK Biodiversity Indicator of habitat
connectivity is state-of-the-art. The reliance on expert judgements to assign permeability and edge values led
to doubts about the indicator’s efficacy when it was first developed. However, the methodology has now been
published in a peer-reviewed journal (Watts & Handley 2010), as has the assignment of permeability values
(Eycott et al. 2011), and experts consulted confirm that the indicator is likely to be correlated with the
population viability of many species, for whichever habitat it is calculated. While the indicator is complex, the
experts suggest that other functional indicators that have been proposed would require even more speciesspecific parameters (e.g. Drielsma & Ferrier, 2009). The proposed presentation of the indicator is intended to
make it readily understood.
Costs of producing indicator: Developing baseline land-cover maps from SPOT scenes and Landsat data may
incur substantial costs. Costs for detecting annual changes in land cover from SPOT scenes and Landsat data
may be significant. Annual costs associated with running the model to produce indicator values are likely to be
significant.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
3.5
Mean connectivity value
3
2.5
All semi-natural habitats
2
1.5
Neutral grassland
1
Broadleaved, mixed and
yew woodland
0.5
0
2010
2013
2014
2015
2016
Year
Figure 4. Example graphic – Indicator of functional connectivity of UK habitats. Connectivity values are on a
scale of 0–100 with typical values for individual habitats being less than 1.
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Evaluation scores for indicator options

All four options have been ranked against the criteria for quality testing indicator options. Sub-totals
of evaluation scores for data issues, the methodology and indicator characteristics are provided in
Table 1. A comprehensive breakdown of scores against individual criteria is presented in Annex 3.

Option A (Species-based indicator of habitat connectivity) scores consistently lower than either
method of implementing Option B (Indicator of structural connectivity) or Option C (Indicator of
functional connectivity). The species data required by Option A is less available than the land-cover
data needed to implement Options B or C. Although the National Forestry Inventory used in Option B
is immediately available, it scores less than the indicator-specific land-cover dataset proposed for
Option C because satellite data collection is judged to be more secure and the data is available over a
longer period. The land-cover data does not score maximum marks only because past coverage relies
on Landsat data, which is at a lower resolution than SPOT scenes or GMES data. The methodology for
Option A is not available and its precision is unknown.

The methodologies for Options B and C have been published in a peer-reviewed journal (Watts &
Handley 2010). However, as neither method of implementing Option B takes account of the
permeability of the matrix between habitat patches, and given the volume of recent literature on the
importance of the matrix for habitat connectivity (Prugh et al. 2009; Prevedello & Vieira et al. 2010;
Eycott et al. 2012), both are considered a proxy in comparison with Option C. As such, both methods
of implementing Option B have been awarded lower scores than Option C for criteria 10-13 (Annex
3).
Option C scores maximum marks on its indicator characteristics except in relation to its complexity,
although it is assumed that it will be accepted by major stakeholders, given that it was supported by
all experts attending the workshop.
Details of how the habitat connectivity indicator maps against the Convention on Biological Diversity’s
(CBD) Aichi Targets, EU Strategy and other CBD indicators in development are presented in Annex 4.
The England biodiversity strategy includes a priority action for which this is a relevant indicator, the
draft Scotland biodiversity strategy proposes priorities that relate. Wales and Northern Ireland are yet
to propose 2020 priority actions or targets.


Table 1. A summary of evaluation scores.
Data issues
Methodology
Indicator characteristics
A
11
2
11
B1
17
6
13
B2
17
6
13
C
17
6
17
4. Discussion Points
It would be helpful if the working group for habitat connectivity at the 6 th Biodiversity Indicators Forum could
provide input on the following:
1.
2.
Which of the options outlined above should be taken forward to the next stage?
Is the presentation of the potential indicators appropriate? How could they be improved?
3. Are there any other ‘easy-to-do’ options which have been missed?
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
5. References
Brommer, J.E. and Fred, M.S. (2007). Accounting for possible detectable distances in a comparison of dispersal:
Apollo dispersal in different habitats, Ecological modelling 209 (2–4), 407–411.
Drieslma, M. and Ferrier, S. (2009). Rapid evaluation of metapopulation persistence in highly variegated
landscapes. Biological Conservation 142, 529–540.
Eycott, A.E., Watts, K., Brandt, G., Buyung-Ali, L.M., Bowler, D.E., Stewart, G.B. and Pullin, A.S. (2008). What is
the evidence for the development of connectivity to improve species movement, as an adaptation to
climate change? - Unpublished contract report to Defra (Defra Contract CR0389). Forest Research,
Farnham, Surrey & Centre for Evidence-Based Conservation, Bangor.
Eycott, A. E., Watts, K., Brandt, G., Buyung-Ali, L. M., Bowler, D., Stewart, G. B. and Pullin, A. S. (2010). Which
matrix features affect species movement? Systematic Review CEE 08-006 (ex SR No. 43). Collaboration
for Environmental Evidence, Bangor, Wales, UK.
Eycott, A. E., Marzano, M. and Watts, K. (2011). Filling evidence gaps with expert opinion: The use of Delphi
analysis in least-cost modelling of functional connectivity. Landscape and Urban Planning 103 (3-4),
400-409.
Eycott, A.E., Stewart, G.B., Buyung-Ali, L. M., Bowler, D. E., Watts, K. and Pullin, A.S. (2012). A meta-analysis on
the impact of different matrix structures on species movement rates. Landscape Ecology.
http://dx.doi.org/10.1007/s10980-012-9781-9.
Hodgson, J.A., Thomas, C.D., Wintle, B.A. and Moilanen, A. (2009) Climate change, connectivity and
conservation decision making: back to basics. Journal of Applied Ecology 46, 964–969.
Powney, G.D., Roy, D.B., Chapman, D., Brereton, T. and Oliver, T.H. (2011). Measuring functional connectivity
using long-term monitoring data. Methods in Ecology and Evolution 2, 527–533.
Powney, G.D., Broaders, L.K. and Oliver, T.H. (2012). Towards a measure of functional connectivity: local
synchrony matches small scale movements in a woodland edge butterfly. Landscape Ecology 27,
1109–1120.
Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. and Brashares, J. S. (2009). Effect of habitat area and isolation on
fragmented animal populations. Proceedings of the National Academy of Sciences of the United States
of America 105, 20770-20775.
Prevedello, J. A. and Vieira, M. V. (2010). Does the type of matrix matter? A quantitative review of the
evidence. Biodiversity and Conservation 19 (5), 1205-1223.
Storfer, A., Murphy, M.A., Evans, J.S., Goldberg, C.S., Robinson, S., Dezzani, R., Delmelle, E., Vierling, L. and
Waits, L.P. (2007). Putting the ‘landscape’ in landscape genetics. Heredity, 98, 128–142.
Tischendorf, L. and Fahrig, L. (2000a). How should we measure landscape connectivity? Landscape Ecology 15
(7), 633–641(9).
Tischendorf, L. and Fahrig, L. (2000b). On the usage and measurement of landscape connectivity. Oikos 90 (1),
7–19.
Watts, K., Handley, P., Scholefield, P. and Norton, L. (2008). Habitat Connectivity – Developing an indicator for
UK and country level reporting. Phase 1 Pilot Study - Unpublished contract report to Defra (Defra
Contract CR0388). Forest Research, Farnham, Centre for Ecology and Hydrology, Lancaster.
Watts, K. and Handley, P. (2010). Developing a functional connectivity indicator to detect change in
fragmented landscapes. Ecological Indicators 10, 552–557.
Watts, K., Handley, P., Eycott, A.E., Peace, A., Marzano, M., Scholefield, P. and Norton, L. (2010). Habitat
Connectivity – Developing an indicator for UK and country level reporting. Phase 2: Production of the
indicator - Defra contract WC0716. Forest Research & Centre for Ecology and Hydrology.
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6. Annexes
Annex 1 – Project overview and approach for developing UK biodiversity indicators
Despite the collective efforts of the biodiversity conservation community to bring attention to biodiversity loss,
pressures on biodiversity are continuing to rise (Butchart et al. 2010, available at
http://www.bipindicators.net/bippublications). Following the 10th Conference of Parties to the CBD in October
2010 and adoption of the new Strategic Plan for Biodiversity (2011-2020), a flexible Pressure, State, Benefits,
Response (PSBR) framework of indicators has been proposed to report on the 20 Aichi targets at multiple
scales (UNEP/CBD/SBSTTA/15/INF/6, available at http://www.cbd.int/doc/?meeting=sbstta-15). There is also a
widespread perception that alternative tools are needed to mainstream issues of biodiversity loss across
sectors through ecosystem services assessment.
The UK has been the first nation to relate ecosystem assessment to ecosystem services, with the recent
publication of the UK National Ecosystem Assessment (UK NEA, 2011, available at http://uknea.unepwcmc.org/Resources/tabid/82/Default.aspx). This initiative, combined with the new Biodiversity Strategic Plan
(2011-2020) championed by the CBD, has resulted in timely emphasis on enhancing the suite of UK biodiversity
indicators to ensure that they continue to be based on the most robust and reliable data, and are relevant to
the new Aichi Targets as well as the revised European Biodiversity Strategy, including requirements for the
Marine Strategy Framework Directive (MSFD) and the Water Framework Directive (WFD).
The Biodiversity Indicators Steering Group (BISG) has proposed an interim set of 24 biodiversity indicators for
reporting against global and European frameworks (http://jncc.defra.gov.uk/page-4229). A number of the
proposed indicators need refinement and/or development. Key challenges will be meeting tight reporting
deadlines and country priorities. To ensure that the UK meets international and national obligations, there is
an imperative to use data currently available.
The major objective of Defra project WC1301 is to construct indicators for six thematic areas identified by the
BISG as requiring development, ensuring that new developments and refinements have a sound scientific
base. These thematic areas are: awareness of biodiversity conservation; status of species and habitats
supporting ecosystem services; habitat connectivity; plant genetic resources; climate change adaptation; and
integrating biodiversity into business activities. In order that the UK retains links from global to country-based
indicators, it will be important that UK indicators can be disaggregated to country level (England, Scotland,
Wales and Northern Ireland) and that they are aligned, as far as practicable, with global and EU frameworks.
The first step in this project was to undertake a scoping exercise to review and synthesize the metadata used
in the UK NEA and identify data sets that might be suitable for developing UK biodiversity indicators. Results of
the scoping exercise informed and guided subsequent, more detailed, data searches and development of
indicator options and methodologies, in consultation with experts.
An options paper has been produced for each thematic area for consideration by: i) invited experts to the UK
Biodiversity Indicator Forum, and ii) the BISG. A codified methodology will be developed for the
implementation of each selected indicator option.
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Annex 2
Table A1. Evaluation scores for sources of land cover data.
National Forest Inventory
Indicator-specific land-cover dataset
(SPOT scenes and Landsat or GMES)
Combined datasets
Sources of data
Land Cover Map
Levels
x
x
x
x
x
x
x
x
x
x
x
x
x
Countryside Survey sample squares
Criteria
1. Transparency
and auditability
2. Verification
3. Frequency of
updates
4. Security
5. Spatial coverage
6. Temporal
coverage
7. Capacity for
disaggregation
1. Data unavailable to public
2. Limited summary data available
3. Full raw/primary data set and metadata
available
1. Unverified data
2. Limited verification checks in place
3. Detailed verification in place and
documented
1. Sporadic
2. Every 3-5 years
3. Annual or biennial
1. Future data collection discontinued
2. Future data collection uncertain
3. Future data collection secure
1. Partial UK coverage
2. UK coverage, some bias
3. Full UK coverage, including adjacent
marine areas, if and where appropriate
1. Insufficient data for assessment (<5
years)
2. Sufficient data to assess progress (5-10
years)
3. Long (10+ years) and short-term trends
can be assessed
1. Cannot be disaggregated
2. Can be disaggregated but data quality
and assessment issues arise
3. Can be disaggregated to Country level
and assessed
13
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
3
Evaluation score
CS sample squares
Land Cover Map
2
National Forest Inventory
SPOT scenes/Land Sat
1
Combined datasets
0
Sources of land cover data
Figure A1. Evaluation scores for sources of land cover data.
14
Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Annex 3
Table A2. Evaluation scores for indicator options. The criteria build upon that provided in the Defra
specification for WC1301 (Developing UK indicators for the Strategic Plan for Biodiversity 2011-2020) with
reference to CBD2, Streamlining European Biodiversity Indicators (SEBI) 3, and Biodiversity Indicators
Partnership (BIP)4 criteria.
Criteria
1. Transparency and
auditability
1. Data unavailable to public
2. Verification
1. Unverified data
2. Limited verification checks in place
3. Detailed verification in place and documented
3. Frequency of updates
Data issues
Levels
4. Security
5. Spatial coverage
6. Temporal coverage
7. Capacity for
disaggregation
A
x
2. Limited summary data available
3. Full raw/primary data set and metadata available
1. Sporadic
2. Every 3-5 years
Methodology
9. Precision
C
x
x
x
x
x
x
x
x
x
x
x
x
x
3. Annual or biennial
1. Future data collection discontinued
2. Future data collection uncertain
3. Future data collection secure
1. Partial UK coverage
2. UK coverage, some bias
3. Full UK coverage, including adjacent marine areas, if and where
appropriate
x
x
x
1. Insufficient data for assessment (<5 years)
2. Sufficient data to assess progress (5-10 years)
3. Long (10+ years) and short-term trends can be assessed
1. Cannot be disaggregated
2. Can be disaggregated but data quality and assessment issues
arise
1. Methodology not available
2. Methodology available but not peer reviewed
3. Methodology published and peer reviewed
1. Unknown precision or precision quantifiable but unable to
statistically assess trends
2. Uncertainty quantifiable and signal-to-noise ratio allows for
statistical assessment of trends
3. Uncertainty quantifiable and signal-to-noise ratio allows for yearon-year statistical assessments
2
x
x
x
x
x
x
x
3. Can be disaggregated to Country level and assessed
8. Transparency and
soundness
Option
B1
B2
x
x
x
x
x
x
x
x
x
x
x
UNEP/CBD/SBSTTA/9/10 (2003). Monitoring and indicators: designing national-level monitoring programmes and indicators. UN Environment
Programme. http://www.cbd.int/doc/meetings/sbstta/sbstta-09/official/sbstta-09-10-en.pdf
EEA (2007). Halting the loss of biodiversity by 2010: proposal for a first set of indicators to monitor progress in Europe. EEA Technical report No
11/2007. http://www.eea.europa.eu/publications/technical_report_2007_11
4
2010 Biodiversity Indicators Partnership (2010) Guidance for national biodiversity indicator development and use. UNEP World Conservation
Monitoring Centre. http://www.bipnational.net/
3
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Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Criteria
10. Policy relevance:
progress towards
Biodiversity 2020 targets
(CBD, EU, UK, country)
1. No clear relationship with 2020 targets
2. Relates indirectly to progress towards 2020 targets
11. Biodiversity relevant
1. Indicator is a proxy for biodiversity change
2. Indicator directly addresses biodiversity and relates indirectly to
state, pressures, benefits and/or responses
3. Indicator directly addresses biodiversity and relates directly to
state, pressures, benefits and/or responses
12. Cause-effect
relationship
Indicator characteristics
Levels
13. Sensitive to change
14. Human-induced vs
natural changes
15. Communication
A
B1
B2
x
x
x
3. Relates directly to progress towards 2020 targets
1. Unknown relationship between indicator and issue of concern
2. Accepted theory of relationship between indicator and issue of
concern
3. Quantifiable relationship between indicator and issue of concern
1. Indicator does not detect changes in systems within timeframes
and spatial scales that are relevant to decision-making
2. Indicator detects changes in systems only within timeframes or
only on spatial scales that are relevant to decision-making
3. Indicator detects changes in systems within timeframes and
spatial scales that are relevant to decision-making
1. Indicator cannot discriminate between human-induced and
natural changes
2. Indicator potentially discriminates between human-induced and
natural changes
3. Indicator clearly discriminates between human-induced and
natural changes
1. Indicator is complex, difficult to communicate and not accepted
by all major stakeholders
2. Indicator is complex and difficult to communicate but accepted
by all major stakeholders
3. Indicator is simple, easy to communicate and accepted by all
major stakeholders
Sub-total: Data issues
Sub-total: Methodology
Sub-total: Indicator characteristics
16
C
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
17
6
13
17
6
13
17
6
17
x
11
2
11
Paper 01 for the 6th UK Biodiversity Indicators Forum Meeting. 5-6 Dec., 2012.
Annex 4 - Relevance to Aichi Targets, EU Strategies and other CBD indicators in
development
Aichi Targets for which this is a primary indicator
Strategic Goal B. Reduce the direct pressures on biodiversity and promote sustainable use.
Target 5: By 2020, the rate of loss of all natural habitats, including forests, is at least halved and where feasible
brought close to zero, and degradation and fragmentation is significantly reduced.
Aichi Targets for which this is a relevant indicator
Strategic Goal C. To improve the status of biodiversity by safeguarding ecosystems, species and genetic
diversity.
Target 11: By 2020, at least 17 per cent of terrestrial and inland water, and 10 per cent of coastal and marine
areas, especially areas of particular importance for biodiversity and ecosystem services, are conserved through
effectively and equitably managed, ecologically representative and well connected systems of protected areas
and other effective area-based conservation measures, and integrated into the wider landscape and
seascapes.
EU biodiversity strategy targets for which this is a relevant indicator
Target 2: By 2020, ecosystems and their services are maintained and enhanced by establishing green
infrastructure and restoring at least 15 % of degraded ecosystems.
England biodiversity strategy priority actions for which this is a relevant indicator
Priority action 1.1: Establish more coherent and resilient ecological networks on land that safeguard ecosystem
services for the benefit of wildlife and people.
Scotland biodiversity strategy (consultation draft, July 2012) priorities for habitats and protected places for
which this is a relevant indicator
Complete the suite of protected places, and improve their connectivity through a national ecological network
centred on these sites.
Wales and Northern Ireland are yet to propose 2020 priority actions or targets.
17