The Future of Healthy Ecosystems
Defra Horizon Scanning Project
SD0306
Revised final report December 2005
Raffaelli, D., White, P.C.L., , Perrings, C.A., Smart, J.S. and A. Renwick
Environment Department
University of York
Correspondence: Dave Raffaelli, [email protected]
Project Code & Title:
SD0306 The Future of Healthy Ecosystems
Representatives of the Parties:
Prof. Dave Raffaelli (Contractor Representative)
Environment Department
University of York
Dr Sara Webster (Secretary of State’s Representative)
Defra
Room 201
Cromwell House
Dean Stanley Street
Westminster
London
Project Dates:
Commencement:
Completion:
Final draft:
Revised final report
Total Project Costs:
Total Staff Input per Grade:
1st March 2004
31st August 2004
October 2004
December 2005
£51,432 excl. VAT
2 PDRA @ 0.5 year
Original objectives:
1.
2.
3.
4.
Review key indicators and definitions of ecosystem health
Identify ecological goods and services associated with different indicators
and evaluate their socio-economic significance
Critically review the modelling approaches available
Assess the potential of higher level metrics and measures that can be used
as possible common currencies and identify knowledge and data gaps
which might impinge on horizon scanning
Milestone
Target Date
Title
01/01
01/06/04
02/01
01/06/04
03/01
01/07/04
04/01
31/08/04
Review the key indicators and definitions of
ecosystem health
Identify the socio-economic significance of
different indicators
Critically review the modelling approaches
available for predicting changes in key indicators
Assess potential for common currencies and
identify knowledge gaps
Executive Summary & Recommendations
1. In the context of global climate change, understanding and monitoring the
function and performance of ecosystems over time is of critical importance.
2. A greater emphasis on environmental sustainability, an accompanying
recognition of the scarcity of natural resources and increasing concern regarding
human impacts on the environment have focused attention on the concept of
‘ecosystem health’ as a method by which such issues might be addressed.
3. This Defra Horizon Scanning Project critically examines the concept of
ecosystem health and makes recommendations as to how the concept might be
implemented within the UK context.
4. A number of different approaches have been used to assess ecosystem health.
These include biological, ecological and biophysical indicators and indices
derived by direct measurement, as well as structural, functional and system-level
metrics derived from ecological and/or economic models of managed
ecosystems.
5. The application of such indicators to assess the health of agricultural, forest and
aquatic ecosystems is described within the UK, Europe and elsewhere.
6. Our review suggests that purely biological or biophysical assessments do not
capture the complexity of managed ecosystems with a significant societal
component, such as agricultural landscapes. Approaches which incorporate
ecological, economic and societal components, such as The Holistic Ecosystem
Health Indicator (HEHI), have more potential in this respect.
7. A new holistic approach, Monitoring Ecosystem Health by Trends Analysis
(MEHTA), is developed here.
8. MEHTA analyses changes in indicators which reflect the status of a set of
environmental, ecological, financial, human and social capital stocks which are
vital for the provision of products of social and economic relevance within
managed ecosystems. MEHTA uses historical data to evaluate the rate and
direction of changes in these capital stocks with respect to desired critical
thresholds.
9. The potential and limitations of the MEHTA approach are illustrated for the North
York Moors National Park and the catchment of the River Ythan in
Aberdeenshire.
10. Our analyses have identified scale mis-match across space and time, and limited
data availability as issues of concern. Further research is required to resolve
these issues.
11. Our key recommendations for policy makers, such as Defra, are as follows.
Whilst the present Defra sustainability indicators make an effort to reflect
ecological, environmental and social dimensions, there is need to develop
approaches which enable these and other measures to be combined in suites or
bundles of sustainability metrics appropriate for particular systems over a range
of spatial scales, in order to deliver a more holistic assessment of the health of
those systems. This will require: research funding to develop the novel statistical
and numerical tools required; consultation with stakeholders about acceptable
limits, thresholds and targets; and agreed changes (where appropriate) in the
kinds of metric data routinely collected by statutory bodies as well as the storage
and management of such data in order to ensure their availability to those
charged with assessing ecosystem health.
CONTENTS
1
Introduction
1.1
1.2
1.3
1.4
1.5
2
Purpose
Objectives
Methods
Structure of the report
Acknowledgements
Origins and development of the ecosystem health concept
2.1 Ecosystem health and the ecosystem approach to conservation
2.2 Ecosystem health and sustainable development
2.3 Relationship of ecosystem health to Quality of Life counts and the UK
Biodiversity Action Plans
1
1
1
2
2
3
3
3
4
4
3
Definitions of ecosystem health
5
4
Approaches to assessing ecosystem health
6
4.1 Costanza’s index of ecosystem health
4.2 Structural and functional indicators
5
Indicators and indices derived from direct measurement
5.1 Ecological indicators
5.2 Multimetric indices
5.3 Issues with biological monitoring and indicators
6
Using models to assess ecosystem health
Odum’s conjectures and mass-balance approaches
Adaptive cycles and resilience
System-level metrics from mass balance models
Mass-balance measures and ecosystem health: a case study of the Ythan
estuary, Aberdeenshire
6.5 Model-based approaches to ecosystem management from ecological
economics, environmental economics and resource economics
6.1
6.2
6.3
6.4
7
6
7
9
9
9
10
11
11
13
14
16
17
Application of different approaches for evaluating the health of terrestrial
and aquatic systems
18
7.1 Agroecosystem health
18
7.1.1 Biological monitoring in agroecosystems
18
7.1.2 Indicators of biodiversity in agroecosystems
20
7.1.3 Population and community ecological parameters as indicators of change
in agroecosystems
21
7.1.4 Means-based and effect-based indicators of environmental impact in
agroecosystems
21
7.1.5 Direct and surrogate indicators in agroecosystems
22
7.1.6 Historical development of agroecosystem indicators in Europe
22
7.1.7 Historical application of agroecosystem indicators within England
25
8
7.2 Forest ecosystem health
7.2.1 Monitoring forest ecosystems
7.2.2 Indicators of forest health
7.2.3 Indicators of forest health in Britain
25
27
27
27
7.3 Aquatic ecosystem health
7.3.1 Physical and chemical indicators of aquatic ecosystem health
7.3.2 Biotic indicators of aquatic ecosystem health
7.3.3 Multimetric biotic indices
7.3.4 Methods used to assess aquatic ecosystem health in Britain
28
30
30
30
31
Interdisciplinary indicators of ecosystem health
8.1 The HEHI approach
8.2 MEHTA (Monitoring Ecosystem Health by Trends Analysis): an alternative
interdisciplinary indicator
8.2.1 Analysing indicator data
8.3 Contrasts with the HEHI approach
9
How the MEHTA approach might be applied
32
32
33
35
36
37
9.1 Case Study 1: The North York Moors National Park
37
9.1.1 Indicators of natural and man-made capital stocks
38
9.1.2 Analysis and results
Error! Bookmark not defined.
9.1.3 Conclusion
43
9.2 Case Study 2: The Ythan catchment in Aberdeenshire
9.2.1 Indicators of natural and man-made capital stocks
9.2.2 Analysis and results
9.2.3 Conclusion
10 Data resolution and availability
10.1 Spatial resolution
10.2 Temporal resolution
10.3 Data availability
11 Conclusions and recommendations
11.1 Is the ecosystem health concept valuable?
11.2 Operational approaches to assessing ecosystem health
11.3 What is the appropriate spatial extent for ecosystem health assessment?
11.4 Data availability and accessibility
11.5 Gaps in knowledge
11.6 Summary of key findings and recommendations
References
44
45
46
48
48
48
50
52
52
52
52
53
53
54
54
55
LIST OF TABLES
Table 4.1
Vigour (V), organisation (O) and resilience (R) components
proposed for use in the construction of an Ecosystem Health Index
(HI), in a variety of settings (based on Table 2 in Costanza 1992)
6
Table 4.2
General criteria for assessing ecosystem health (Xu & Mage, 2001)
7
Table 6.1
Odum’s 24 attributes of development through ecological
succession (Odum, 1969)
11
Table 7.1
Twelve different agroecosystem evaluation methods (after van der
Werf and Petit (2002))
18
Table 7.2
Proposed ecological parameters to monitor changes in
agroecosystems (Buchs et al., 2003)
20
Table 7.3
State and pressure indicators selected by ELISA (after Wascher,
2000)
22
Table 7.4
Indicators related to agricultural practice, proposed by the PAIS
(LANDSIS g.e.i.e. et al., 2000)
23
Table 7.5
Factors that contribute to forest health (after Percy, 2002)
25
Table 7.6
Indicators designed and used by the UK Forestry Commission
(Forestry Commission, 2002)
27
Table 7.7
Ecological indicators used to assess structural, functional and
system-level determinants of ecosystem health (after Xu et al.
2001)
28
Table 9.1
Summary of trends in indicators of natural and man-made capital
stocks in managed ecosystems within the NYMNP
38
Table 9.2
Summary of trends in indicators of natural and man-made capital
stocks in the Ythan catchment
43
LIST OF FIGURES
Fig 6.1
The adaptive cycle of ecosystem behaviour (From Gundersen and
Holling, 2002)
13
Fig 8.1
Structure of the Holistic Ecosystem Health Indicator (HEHI) used by
Aguilar (1999) in tropical managed systems in Costa Rica
32
Fig 8.2
Assessing ecosystem health by determining trends and safety
margins for individual indicators of the status of environmental,
ecological, financial, human and social capital stocks within a
managed ecosystem. The MEHTA (Monitoring Ecosystem Health
by Trends Analysis) approach
34
Fig 9.1
Actual and predicted number of visitor days (million) within the
North York Moors National Park showing 95% upper and lower
confidence intervals for individual predictions
39
Fig 9.2
Actual and predicted breeding bird survey index from the Grampian
region showing 95% upper and lower confidence intervals for
individual predictions
44
Fig 10.1a
Spatial resolution of different indicator datasets for the North York
Moors National Park
46
Fig 10.1b
Spatial resolution of different indicator datasets for the Ythan
catchment
47
Fig 10.2a
Temporal resolution of different indicator datasets for the North
York Moors National Park
48
Fig 10.2b
Temporal resolution of different indicator datasets for the Ythan
catchment
48
1
1
1.1
Introduction
Purpose
The need to understand and quantify ecosystem behaviour and condition has come
to the forefront of environmental policy due to a greater emphasis on environmental
sustainability and an accompanying recognition of the scarcity of natural resources,
such as water, soil and biological diversity. Increasing concern regarding human
impacts on the environment and the possibility that some human-induced changes in
ecological systems may be irreversible has also focused attention on ways in which
such changes can be assessed and, if possible, avoided. From the policy maker’s
perspective, the concern is not only in terms of the possible extent of these problems,
but also the likelihood of their occurrence and the timeframe over which they may
operate. In the context of global climate change, understanding the functioning of
ecosystems, and how their health and performance can be measured and monitored
over time is of critical importance. This need has also been highlighted recently by
the recommendations of the Millennium Assessment: ecosystem performance and
human activity and well-being are inseparable.
At this stage, it is worth rehearsing some definitions for the reader. The term
“ecosystem” has a long history in ecology and many definitions. Here, we adopt the
perspective of the Convention on Biodiversity which defines ecosystem as ….” a
dynamic complex of plant, animal and micro-organism communities and their nonliving environment interacting as a functional unit.” (Article 2 of the convention), and
in order to accommodate the ecosystem approach to biodiversity conservation (see
below) the recognition that “humans, with their cultural diversity, are an integral
component of ecosystems”. Our definition of an ecosystem thus combines the
natural and human elements present, referred to as the natural and social capital
stocks which provide the services from which humans benefit. Ecosystem services
can be provisioning (e.g. food, fresh water, fuel wood, genetic resources), regulating
(e.g. pest control, pollination, flood control) or cultural (e.g. spiritual, aesthetic,
educational, symbolic). An ecosystem is healthy if it is able to maintain those flows of
services which underpin human well-being as well as its resilience to perturbations.
One approach to the issue of assessing the ability of an ecosystem to continue to
deliver services is through the concept of “ecosystem health”, a field dominated to
date by North American researchers, and which understandably emphasises North
American ecosystems and environmental policy. This report builds on this previous
work and assesses the potential of the ecosystem health concept for monitoring
change in the UK landscape, in particular the extent to which health assessment can
be linked to an ecosystem’s ability to produce marketed and non-marketed products
which are of socio-economic value, and how sustainability might be related to the
continued capability to deliver such products.
1.2
Objectives
The four principal objectives of the work were to:
1. Review the key indicators and definitions of ecosystem health
currently under consideration, outlining their relative advantages and
disadvantages and identify possible future indicators of ecosystem
health.
2
2. Identify the ecological goods and services associated with different
indicators and evaluate their socio-economic significance.
3. Critically review the modelling approaches available for predicting
changes in key indicators and in the services delivered by the
ecosystems to which they belong; and for assessing the risks
associated with such changes.
4. Assess the potential for a variety of higher-level metrics and measures
that can be used as possible common currencies, and identify those
gaps in our knowledge and data which might impinge on horizon
scanning in the short, medium and longer term.
1.3
Methods
The approach used was a combination of extensive literature reviews, dialogue with
key researchers in the area, and case studies to illustrate the operational potential,
as well as limitations, of specific approaches to assessing ecosystem health.
1.4
Structure of the report
The report tracks the progression of the ecosystem health concept through time,
originating as a purely bio-physical measure to become a holistic tool that
incorporates social as well as ecological aspects. The report is structured as follows:

Section 2 discusses the origins and development of the ecosystem health
concept, including relationships with the ecosystem approach to conservation
and sustainable development, and the UK Government’s sustainable
development headline indicators

Section 3 discusses various definitions of ecosystem health

Section 4 reviews general approaches which have been used to assess
ecosystem health

Section 5 reviews single and multi-metric approaches to quantifying
ecosystem health within ecological and socio-economic frameworks

Section 6 discusses the use of models and system-level approaches for
assessing ecosystem health

Section 7 describes the application of these different approaches for
evaluating the health of terrestrial and aquatic systems

Section 8 discusses the potential of holistic and interdisciplinary indicators of
ecosystem health: the Holistic Ecosystem Health Indicator (HEHI; Aguilar,
1999), and an alternative approach, Monitoring Ecosystem Health by Trends
Analysis (MEHTA)

Section 9 shows how the MEHTA approach can be applied to two welldocumented and contrasting UK-based case studies, in order to identify
knowledge gaps

Section 10 discusses these knowledge gaps, in particular issues of data
resolution and availability which are central to the evaluation of ecosystem
3
health, based on the preceding literature reviews and the findings from the
two case studies

1.5
Section 11 draws conclusions on the validity and usefulness of measuring
ecosystem health, potential policy implications and suggests priorities for the
future development of the ecosystem health concept in the UK, and its
relevance to UK policy, including the Biodiversity Action Plans and
sustainable development
Acknowledgements
The project was funded by the Department for Environment, Food and Rural Affairs
(Defra). We also thank D. Rapport, R. Costanza and C.S. Holling for helpful
discussions on ecosystem health developments in North America, and the Defra
steering group for this project for their helpful comments and suggestions. Several
anonymous reviewers provided helpful comments and suggestions on the final draft.
2
Origins and development of the ecosystem health concept
The general concept of “healthy” and “unhealthy” ecological systems has been in
common usage for decades amongst ecologists, but attempts to formalise the
concept and provide an operational definition are relatively recent. The most
significant milestones in this respect were a workshop at the University of Illinois in
1991 on Ecosystem Medicine: developing a diagnostic capability, a second workshop
at the Aspen Institute, Maryland, in October 1990, and a symposium sponsored by
the International Society for Environmental Ethics, at Washington, D.C., in 1991. The
material presented at, and ideas emerging from, the latter two meetings are
packaged within Costanza, Norton and Haskell’s book Ecosystem Health (1992),
which represents the first coherent attempt to develop ecosystem health as a
rigorous scientific discipline and which laid the foundations for much of the work that
has followed.
The Ecosystem Medicine workshop spawned the International Society for Ecosystem
Health (Rapport et al., 1999) and the society produced the journal Ecosystem Health,
(now lapsed), which has now been replaced in 2004 by a new journal EcoHealth that
has a similar coverage (ecohealth.net).
2.1
Ecosystem health and the ecosystem approach to conservation
Ecosystem health has implicit associations with the ecosystem concept in the areas
of conservation and management, as well as with the ideas surrounding
sustainability. For instance, the Convention on Biological Diversity defines the
ecosystem approach as “…..a strategy for the integrated management of land, water
and living resources that promotes conservation and sustainable use in an equitable
way. Application of the ecosystem approach will help to reach a balance of the three
objectives of the Convention. It is based on the application of appropriate scientific
methodologies focused on levels of biological organization which encompass the
essential processes, functions and interactions among organisms and their
environment. It recognizes that humans, with their cultural diversity, are an integral
component of ecosystems.” Thus, in the ecosystem approach, the relationships
between humans, biodiversity and the products derived from services supplied by
4
ecosystems are articulated at the ecosystem scale. The inclusion of the human
dimension within the ecosystem, as opposed to part from the system, are further
articulated in the CBD’s Malawi principles. Similarly, The State of the Nation’s
Ecosystems programme in the US (www.heinzctr.org/ecosystems) aims to include
human usages (products of socio-economic value) provided by those systems,
although many of the indicators for these aspects have yet to be developed within
that programme. Similarly, the Millennium Assessment has stressed the relationships
between biodiversity, ecosystem processes and goods and services, as well as those
feedbacks between social and ecological systems which demand a new perspective
on ecosystem assessment and monitoring – the coupled socio-ecological system.
There are few operational tools available to assess and monitor such systems, but
the holistic measures of ecosystem health have potential in this respect.
2.2
Ecosystem health and sustainable development
Human economic development depends on a range of ecosystem services which are
supported by stocks of natural and social capital resident within ecosystems. This
implies that for economic development to be truly sustainable the flow of products of
socio-economic value delivered by ecosystem services must be maintained. From an
ecological perspective, ecosystem health should reflect an ecosystem’s capacity for
renewal, i.e. its resistance to, and ability to recover from, perturbations. Ecosystem
health could therefore be considered as a measure of the ecosystem’s ability to
continue to provide those ecosystem services, i.e. as a measure of an ecosystem’s
sustainability.
This definition of ecosystem health has often been limited to the ecological
component of ecosystems, and humans are implicitly considered as external agents
that affect the system. In terms of sustainable development, and because human’s
now influence all the world’s ecosystems, this restricted definition is unhelpful. In
Europe, less than 3% of the landscape remains un-dominated by agriculture, forestry
or urban development and here at least, humans are an integral part of the system
and cannot be considered apart from it (Piorr, 2003). A full assessment of ecosystem
health must acknowledge this fact. Moreover, maintaining healthy ecosystems
capable of supporting ecosystem services which deliver products of socio-economic
relevance is essential if the social well-being of current and future generations is to
be maximised.
In this report, we also consider ecosystem health in this broader context, as a
measure of social welfare or utility. Under this definition, a healthy ecosystem is one
which retains sufficient stocks of natural capital to support ecosystem services which
deliver a wide range of products which are of value and relevance to society. The
products concerned may have market value (e.g. agricultural and forestry crops, fish
catches, wildlife harvests), or may provide various forms of non-market value (e.g.
clean air, scenic views, cultural and social identity).
2.3
Relationship of ecosystem health to Quality of Life counts and the UK
Biodiversity Action Plans
Ecosystem health has resonances with the intentions behind the UK Government’s
Quality of Life counts and sustainable development indicators (Defra 2005).
However, one key difference is that the UK government targets are defined
nationally, whereas ecosystem health is specifically concerned with smaller,
biologically functional areas. A second more meaningful difference is that a proper
assessment of ecosystem health could potentially provide a more holistic
representation of sustainable development that incorporates the values and
5
aspirations of society. In other words, what are the thresholds or limits of ecological
and social change which society deems acceptable?
The emphasis of ecosystem health (and the Quality of Life counts) on higher-level
metrics is quite different from that of the UK Biodiversity Action Plan (UK BAP). The
focus of UK BAP is very much on individual rare species and habitats, and the
implicit value of UK BAP is measured in terms of the conservation of species or
habitat richness through maintaining and enhancing the distribution and/or
abundance of these species or habitats. There is no assessment of the contribution
which these species make to maintaining ecosystem functions and processes, or of
their contribution to social welfare. In contrast, these latter aspects are at the
forefront of the assessment of ecosystem health. The assessment of ecosystem
health and the UK BAP programme are therefore complementary and both have an
important role to play in the conservation of biodiversity.
3
Definitions of ecosystem health
A considerable body of literature over the past decade has sought to define
ecosystem health in practical terms, and attempts have been made to apply the
concept to a wide variety of terrestrial, freshwater and marine systems. Many
definitions exist and many share common components (Rapport, 1995). These stem
largely from the discipline of stress ecology (Odum, 1985), which defines health in
terms of organisation (structure, e.g. how many and types of species), resilience (the
disturbance a system can withstand without changing its ability to organise itself
(Holling, 1973), or the ability of a system to return to its former condition following
disturbance (Pimm, 1984, 1991)), vigour (function, e.g. activity, metabolism or
primary productivity (Mageau, Costanza & Ulanowicz, 1995)), and absence of
ecosystem distress signs (an absence of “illness”)(Costanza, 1992).
The majority of the definitions of ecosystem health concentrate exclusively on
ecological aspects of the term. For example, Costanza (1992) defines the term as
follows: ‘An ecological system is healthy and free from “distress syndrome” if it is
stable and sustainable – that is, if it is active and maintains its organisation, and
autonomy over time and is resilient to stress’. It is, however, important that
biological, economic and human dimensions of the system are also considered (see
section 2) and the International Society for Ecosystem Health incorporated all these
relevant dimensions in its definition: ‘a systematic approach to the preventive,
diagnostic, and prognostic aspects of ecosystem management, and to the
understanding of relationships between ecosystem health and human health…’
(Rapport et al., 1999). Xu and Mage (2001) also included each of these dimensions
in their definition of the health of managed systems: ‘the system’s ability to realise its
functions desired by society and to maintain its structure needed both by its functions
and by society over a long time.’ This definition considers both functional (activities
and processes, which occur within an ecosystem, for example gross productivity) and
structural characteristics (individual components of the system and their
interrelationship, for example species diversity) of the ecosystem in the context of
societal needs as well as emphasising the importance of temporal changes (Xu &
Mage, 2001).
6
4
Approaches
A number of different approaches have been taken to assess ecosystem health, two
of which are discussed below.
4.1
Costanza’s index of ecosystem health
Costanza (1992) proposed that a full assessment of the ‘health’ of an ecosystem
would account for the following six attributes of the system concerned: (1)
homeostasis (self-regulation); (2) absence of disease; (3) diversity or complexity
(number and types of species); (4) stability or resilience; (5) vigour or scope for
system growth; and (6) balance between system components. He considered it
necessary to address all, or at least a majority, of these attributes simultaneously and
proposed an index (HI) which reflected the ability of a healthy and sustainable
system to maintain its metabolic activity level (‘system vigour’ V), as well as its
internal structure and organisation (‘system organisation’ O), and to be resilient to
outside stresses (‘system resilience’ R) over the spatial and temporal frames of
reference. Thus
HI  V  O  R
Application of Costanza’s ecosystem health index requires the assessment of vigour,
structure and resilience in a quantified and commensurable fashion in real
ecosystems. Each individual component poses different challenges for quantification.
Costanza suggested that it might be appropriate to use different quantities as
measures of vigour, organisation and resilience within different ecosystem settings
(Table 4.1) depending on the data available and the perspective or value system
adopted for the health assessment. However, such flexibility could impede ready
comparison of ecosystem health indices between ecosystem settings and could also
introduce weighting difficulties between the vigour, organisation and resilience
components within the health index in any particular setting.
Table 4.1 Vigour (V), organisation (O) and resilience (R) components proposed for
use in the construction of an Ecosystem Health Index (HI), in a variety of settings
(based on Table 2 in Costanza (1992))
Component
Related
Concepts
Potential
Measures
Field of
Origin
Assessment Method
Vigour
Function
Productivity
Throughput
GPP, NPP, GEP
GNP, NNP *
Metabolism
Ecology
Economics
Biology
Direct measurement
Estimation from data
System modelling
Organisation
Structure
Biodiversity
Diversity index
Connectance
Ecology
Network analysis
Proof against
change of state
Return time
Ecology
Economics
Simulation modelling
Resilience
* Gross primary production, net primary production, gross ecosystem production,
gross national product, net national product, respectively.
7
4.2
Structural and functional indicators
A related approach advocated by Xu and Mage (2001) uses four sets of general
criteria to assess the health of managed ecosystems: structural, functional,
organisational (condition of ecosystem relative to its relationships with the external
environment), and dynamics (the temporal aspect of the ecosystem and its ability to
cope with change) (Table 4.2). Structural and functional changes within a stressed
ecosystem will induce corresponding changes in system level attributes, such as
ascendancy, buffer capacities, exergy and structural exergy, terms which will be
unfamiliar to many readers and which are defined below (Jørgensen, 1997). Okey
(1996) also used a similar set of properties to assess ecosystem health. These
comprised stability, resilience, sustainability, self-organisation, diversity/complexity
(the structural and functional variability of an ecosystem), efficiency, and equitability.
Table 4.2 General criteria for assessing ecosystem health (Xu & Mage, 2001)
Criteria
Related
concepts
Definition
Relation to
health
Examples of
measures
Structural
Resource
availability
Volume of
resources
necessary to
achieve or maintain
system functions
Higher level of
resource
availability
considered
healthier
Crop
production
Resource
accessibility
Ease of access to
and utilization of
system’s resources
High accessibility
to all resources
considered
healthier
Accessibility of
agricultural
land to water
(Ali, 1995)
Diversity
Number of system
components and
how they vary
across space
More diverse
ecosystem is
healthier
Number of
species and
size of
population
Equitability
Evenness of the
distribution of
ecosystem
products across
society (may be a
measure of
diversity)
Subset or a
measure of
diversity (limited
use in health
assessments)
Land resource
distribution
among
households
(Conway,
1985)
Equity
Distributive fairness
of resources
among people
across space and
time.
Very broad and
general concept
(limited use)
Productivity
Output of product
per unit of resource
input
Higher
productivity is
healthier
Functional
Energy output
per hectare
8
Efficiency
Ratio of output
(product) to input
(cost)
Highly efficient
system is
healthier
Energy ratio,
yield, net
income per unit
of resource
(Conway,
1987)
Effectiveness
Capacity of the
system to meet
goals of
stakeholder
More effective
system is
healthier
Monitoring the
outcome of
implemented
programmes or
schemes, e.g.
Entry level
scheme
(Table 4.2 continued over page)
Table 4.2 contd.
Criteria
Criteria
concepts
Definition
Organisational
Integrity
No commonly
accepted definition
Selforganisation
Degree to which a
system maintains
its organisation
Largely
conceptual, no
practical measure
Autonomy
Ability of system to
absorb external
disturbance by self
reorganising its
structural and
functional units and
continue to function
by self-regulating
the flow of energy,
information and
materials
Similar to selforganisation
Questionnaire
survey to
determine state
of rural
communities
despite
agricultural
intensification
in the area
Selfdependence/
self-reliance
Relationship of
system’s
organisation with
the external
environment
Greater selfdependence is
healthier
Fossil fuel input
per hectare
Stability
Ability of a system
to attain or retain
an equilibrium
steady state
(Holling, 1986)
More stable is
healthier
Qualitatively
measured by
considering
impacts of the
socio-economic
and biophysical
environments
(Bayliss-Smith,
1982)
Dynamics
Relation to
health
Examples of
measures
9
5
5.1
Resilience
Ability of an
ecosystem to cope
with natural and
socio-economic
stresses (WaltnerToews, 1994)
More resilient is
healthier
Capacity to
respond
Systems capacity
to respond to
various stresses
Essentially
synonymous to
resilience
Very few
practical or
numerical
measures of
resilience
Indicators and indices derived from direct measurement
Ecological indicators
Ecological indicators are commonly used to determine the impact of various
environmental contaminants and disturbances, and such indicators have been
applied to assess the overall health of an ecosystem (Xu, Jorgensen & Tao, 1999).
Several different types of ecological indicators exist and those chosen depend on
their required role in the assessment process. Ecological indicators can be divided
into three classes: 1) early warning indicators that detect impending changes; 2)
compliance indicators that detect changes in characteristics beyond acceptable
limits; and 3) diagnostic indicators that show the causes of deviations (Boulton,
1999). An ideal indicator should incorporate the following characteristics to provide a
holistic interpretation of the status of the monitored system (Boulton, 1999):
1) relevant, robust and scientifically supported (Walker, 2002); 2) readily repeatable
and easily validated; 3) relatively cheap and quick to measure; 4) amenable to
measurement by non-trained persons; and 5) able to inform ecosystem managers
and policy makers about the state of the ecosystem (Bockstaller & Girardin, 2003).
An indicator should also be unambiguous in its response to threats to the system,
although this may not always be possible given the complexity of the systems being
monitored (Sueter, 1993). Indicators should use standard units (van der Werf &
Petit, 2002) and be tested and calibrated against empirical measures to determine
their validity (Duelli & Obrist, 2003). This validation process is important, but it is
rarely implemented (Bockstaller & Girardin, 2003). Surrogate indicators are often
used to simplify data collection and case studies of well-known ecosystems may be
used to evaluate the utility of such simplifications (van der Werf & Petit, 2002; Buchs,
2003a).
The usefulness of ecological indicators depends on the approach and spatial scale
adopted as well as their practicality in use. Ecosystems can potentially exist in
multiple dynamic states (Patil et al., 2002), and ecological indicators are therefore
required to provide meaningful assessments in the face of such changes. Timeseries data are often required and spatial scales ranging from habitat patches to
landscape, regional and global scales need to be considered (Waldhardt, Simmering
& Albrecht, 2003) in order to address issues at the ecosystem, regional, national and
international levels.
5.2
Multimetric indices
Purely biological assessments which use a single or limited number of species are
insufficient to capture the complexity of living systems (Buchs, 2003a) and human
dependence on them (Haskell, Norton & Costanza, 1992; Rapport et al., 1999). More
holistic approaches using sets of indicators that incorporate economic, ecological and
10
societal components have therefore been devised (Stork & Eggleton, 1992; Stork,
1995; Ferris & Humphrey, 1999; Aguilar, 1999). Each individual indicator is selected
to represent a different aspect of ecosystem health, and the simultaneous use of
several indicators provides a better measurement of the overall health of the
ecosystem which encompasses both biophysical and socio-economic aspects (Karr,
1992; Rapport, 1995; Jørgensen, 1997). The values of individual indicators can then
be amalgamated to produce a multimetric index (Duelli & Obrist, 2003), e.g. gross
ecosystem product (GEP) (Hannon, 1985); ecosystem stress indicators (Rapport,
Regier & Hutchinson, 1985); the index of biotic integrity (IBI) (Karr et al., 1986);
Costanza’s overall index of ecosystem health (HI) (Costanza, 1992); buffer capacities
(Jørgensen, 1995; Jørgensen, Nielsen & Mejer, 1995). Jørgensen (1997) proposed
that a number of these composite indices could be analysed simultaneously to obtain
a full picture of ecosystem health. However, combining separate indicators into a
multimetric index must be done with care, particularly if the separate indicators do not
track each other with respect to health. Hoffmann and Greef (2003) and Hoffmann et
al. (2003) developed a mosaic indicator approach, based on qualitative and
quantitative assessments that acknowledged the historical development of the
landscape. In a different approach, Bockstaller, Girardin and van der Werf (1997)
proposed the use of sustainability polygons, webs, or radars to overcome these
issues and to aid in visual presentation of outputs (Gomez et al., 1996; Swete-Kelly,
1996; Bockstaller, Girardin & van der Werf, 1997). These representations show the
scores of each index component simultaneously and thus prevent the need to
aggregate scores across different scales (Rigby et al., 2001). The resultant picture
integrates all the behaviour and processes of the separate elements within the
biological system (Karr, 1999). Multivariate statistical approaches have also been
used to capture patterns of change in the assemblages of species present under
different disturbance levels. For example, aquatic invertebrate assemblages can be
sampled from streams and rivers and the relative abundance of the species present
compared with that expected under undisturbed conditions, deviations from the
expected implying a disturbed system. Several such packages have been developed,
for example, RIVPACS (Wright, 1995), AusRivAS (Parsons & Norris, 1996), and
BEAST (Reynoldson et al., 1995)) and they can be used as multimetric indices of the
status of at least part of the ecosystem. Multimetric indices for evaluating the health
of aquatic systems are further considered in section 7.3.
5.3
Issues with biological monitoring and indicators
The very complexity of ecosystems makes the assessment of ecosystem health
challenging. Different systems respond uniquely to stress and have certain unique
features vital for their individual integrity (Rapport et al., 1999). Previous attempts to
monitor ecosystem health have experienced a number of problems that limit their
usefulness: determining which features characterise a healthy ecosystem
(Belaoussoff & Kevan, 1998); absence of important data (knowledge gaps);
restriction of studies to small areas (Wichert & Rapport, 1998); natural fluctuations in
the system (Buchs, 2003a); determination of baseline reference points (Buchs,
2003a); lack of appropriate analytical methods (Patil & Myers 1999; Patil et al.,
2002); and the failure to integrate human, social and ecological dimensions (Epstein
& Rapport, 1996; Huq & Colwell, 1996). The conditions within a site are essentially
unique and thus results obtained for a particular site cannot be extrapolated across
locations. In order to quantify the magnitude and direction of change within the
system it may be possible to relate results to a presumed control site (Bartels &
Kampmann, 1994; Holland et al., 1994; Steinmann & Gerowitt, 2000 cited in Buchs,
2003a), but suitable control sites are often difficult to find. Long-term monitoring
programmes may be required to detect environmental change but this is labour
intensive and expensive (Piorr, 2003).
Financial restrictions dictate that a
11
compromise must generally be drawn between the precision and accuracy delivered
by a particular indicator or monitoring technique, the implementation time required
and its ease of use by non-specialist personnel (Buchs, 2003a).
6
Using models to assess ecosystem health
Indices and indicators of ecosystem structure and function are typically derived by
direct measurement, followed in some cases by appropriate calculation. System-level
metrics which have been used to assess ecosystem health are, however, generally
derived from models of the ecosystem concerned. Such models should embody key
ecosystem components, reflect their interrelationships appropriately and be
calibrated using data from relevant study sites. In addition, model predictions should
be verified by supporting measurements. Once appropriate calibrated models have
been constructed, then the desired system-level metrics can be determined.
Jørgensen (1997) suggested that structural, functional and system-level metrics
should be applied simultaneously and this dictates that direct measurements and
appropriate ecological modelling should be undertaken together to produce a reliable
assessment of ecosystem health on the basis of Costanza’s six attributes (section
4.1). Advances in statistical and computational methods which allow both spatial and
temporal aspects of indicators to be represented may facilitate the success of
ecosystem health assessments (Patil, 2000; Patil, 2001c, b, a; Johnson et al., 2002).
One group of models that has potential for ecosystem health assessment are those
associated with systems ecology. Such models represent the trophic networks that
connect different species in a system. Higher-level properties can be calculated from
the complexity of the network and the magnitude of flows of material or information
through the network, and these properties can be related to aspects of ecosystem
health. Much of the terminology and concepts stem from the work of Eugene Odum
(Odum 1953 – see also Odum 1969, 1985), oft described as the "father of modern
ecosystem ecology”. His conjectures concerning the ways in which ecosystems
move away from their thermodynamic equilibrium as they develop and “mature”
underpin much of the modelling approach to ecosystem health.
6.1
Odum’s conjectures and mass-balance approaches
Over the past 50 years, ecosystem ecologists have described a range of system
attributes that may have potential as indicators of ecosystem health. These include
many of the original 24 conjectures of Odum (1969) and developments thereof, such
as network ascendancy (which can be thought of as the product of the amount of
material flowing through a system and that system’s complexity) (Ulanowicz 1986,
1992) and exergy (which can be thought of as the ‘useful’ energy which must be
dissipated in order to sustain an ecosystem) (Jørgensen, 1995; Nielsen & Ulanowicz,
2000). Central to these concepts is the view that ecosystems move progressively
through developmental stages towards their climax state, culminating in a stable
system with maximum biomass and/or ‘information’ and optimal utilisation of
available energy through internalisation of material flows and increased feedback
control as the system matures (Table 6.1).
Analogues of many of these
measurements can be determined in a straightforward manner from a mass balance
or a network model of the ecosystem under investigation (Christensen, 1995;
Christensen & Walters, 2004), as described in section 6.3. However, worldviews of
stability and ecosystem development exist which differ from those of Odum and
these are briefly described in the next section.
12
Table 6.1 Odum’s 24 attributes of development through ecological succession
(Odum, 1969).
Ecosystem attributes
Community energetics
1.
Gross production/community
respiration (P/R ratio)
2.
Gross production / standing crop
biomass (P/B ratio)
3.
Biomass supported / unit energy flow
4.
Net community production (yield)
5.
Food chains
Community structure
6.
Total organic matter
7.
Inorganic nutrients
8.
Species diversity – variety
component
9.
Species diversity – equitability
component
10. Biochemical diversity
11. Stratification and spatial
heterogeneity (pattern diversity)
Life history
12. Niche specialisation
13. Size of organism
14. Life cycles
Early stages
Mature stages
Greater or less than 1
Approaches 1
High
Low
Low
High
Linear,
predominantly grazing
High
Low
Web-like,
predominantly detritus
Small
Extrabiotic
Low
Large
Intrabiotic
High
Low
High
Low
Poorly organised
High
Well organised
Broad
Small
Short, simple
Narrow
Large
Long, complex
Table 6.1 continues overleaf
Table 6.1 contd.
Ecosystem attributes
Nutrient cycling
15. Mineral cycles
16. Nutrient exchange rate between
organisms and the environment
17. Role of detritus in nutrient
regeneration
Selection pressure
18. Growth form
19.
Production
Overall homeostasis
20. Internal symbiosis
21. Nutrient conservation
Early stages
Mature stages
Open
Rapid
Closed
Slow
Unimportant
Important
For rapid growth
(“r-selection”, e.g
weeds)
Quantity
For feedback control
(“K-selection”, e.g trees)
Undeveloped
Poor
Developed
Good
Quality
13
22.
23.
24.
6.2
Stability (resistance to external
perturbations)
Entropy
Information
Poor
Good
High
Low
Low
High
Adaptive cycles and resilience
A somewhat different perspective of ecosystem development and behaviour to that of
traditional systems theory comes from recent developments in the fields of resilience
and adaptive cycles (Holling, 1973; Holling, 1992; Gunderson & Holling, 2002;
Gunderson & Pritchard, 2002). Here, ecosystems are characterised by an adaptive
cycle of change that has four main phases (Fig 6.1). Two of these phases,
exploitation and conservation are characterised by species with r (high growth rates,
high fecundity, short-lived, high dispersal, low competitive ability – “weeds”) and K
(slow growth, lower fecundity, high competitive ability, long lived – “trees”) strategies,
respectively. These two phases have clear parallels with Odum’s (1969) ideas about
the life forms that characterise early and late developmental stages (Table 6.1).
However, a unique feature of the adaptive cycle is that external and/or intrinsic
perturbations (e.g. hurricanes, drought, pests and disease outbreaks) cause a
sudden and catastrophic release of the accumulated biomass and materials in the
system, the release phase (for example, a fully developed and mature forest may
collapse). This material is then re-organised as it becomes opportunistically captured
by pioneer species. In a sustainable system, the resources accumulated during the
conservation phase, which determine the ecological potential of the system, may
generate a similar ecosystem following re-organisation. However, if specific
accumulated resources (e.g. key taxa or soil conditions) are dramatically changed
during the release phase, then the system is expected to develop quite differently
following re-organisation: the system would “flip” into a different state.
Resilience of the system (its ability to cope with perturbations) will vary throughout
the adaptive cycle as the system moves between the four phases. Thus, resilience is
high in the re-organisation and exploitation phases, but declines during the
conservation phase with the system becoming more vulnerable to “surprises” (fire,
drought, disease) as it becomes more rigid and inflexible.
This view of ecosystem behaviour is proving attractive to many ecologists and
economists, partly because of the parallels and analogies which can be drawn with
the behaviour of economic and social systems, thereby providing an opportunity for
integrating human and ecological systems (e.g. Gunderson and Holling, 2002), and
partly because there is mounting evidence of such cyclic behaviour and alternate
states in real ecological systems (reviewed in Gunderson and Holling, 2002;
Gunderson and Pritchard, 2002; references therein). Whilst adaptive cycles provide a
powerful conceptual framework in which to think about how best to approach
sustainable management of ecological and socio-economic systems (e.g. Gallopin,
2002; Holling, Gunderson & Peterson, 2002), we are a long way from developing
operational measures of ecosystem health based on this theory.
14
Reorganisation
Conservation
Exploitation
Release
Fig 6.1 The adaptive cycle of ecosystem behaviour (From Gunderson and
Holling, 2002)
6.3
System-level metrics from mass balance models
Mass balance models are relevant to ecosystem health because they provide a
quantified description of the structure and function of ecosystems in their steady
state, a starting point for the assessment of the health, and they can be used to
explore the mechanisms which underpin ecosystem growth and development. An
implicit link exists between an ecosystem’s ability to follow its ‘normal’ path of growth
and development (Table 6.1) and its ‘state of health’. Thus, Ulanowicz (1992) defines
a healthy ecosystem as ‘one whose trajectory toward the climax community is
relatively unimpeded and whose configuration is homeostatic to influences that would
displace it back to earlier successional stages’.
The construction of mass balance models is facilitated by the readily available
software package Ecopath (Fisheries Centre University of British Columbia, 2004),
although other network software is also available, e.g. NET-WRK 4.2a (Ulanowicz &
Kay, 1991). Marine fisheries provided the initial application area for such models, and
marine implementations still comprise the majority of installations (see Ecological
Modelling Special Issue 172, 2004), although Ecopath models have now also been
published for freshwater and terrestrial ecosystems (Christensen, 1995; Dalsgaard,
Lightfoot & Christensen, 1995; Ruesink, Hodges & Krebs, 2002).
The Ecopath approach to mass balance seeks to establish a balance in biomass
production and consumption between user-defined groups (age-classes within a
species, individual species or groups of species) in the trophic structure of the
ecosystem, and also to establish a balance in energy flow within each group. The
model thus requires basic estimates of the biomass, production and consumption of
all groups. Some parameters can/must be estimated by the software itself, and there
are routines that allow an assessment of parameter uncertainty. A full account of
15
Ecopath and mathematical descriptions of the ecosystem metrics which it produces
can be found at www.ecopath.org.
The approach is data-intensive for a large complex system so that simplified or
aggregated groupings tend to be employed rather than individual species (although
changes in taxonomic resolution of groupings may affect the value of some of the
higher level metrics that are derived (Abarca-Arenas and Ulanowicz 2002).
Parameters can be derived by empirical investigation or by reference to other
databases or established relationships, such as that between body size and
production per unit biomass (P/B ratio).
Various metrics can be derived from mass balance models that might reflect
ecosystem health in terms of its vigour (productivity), organisation (structure) and
resilience (c.f. section 4.1), and which are analogous to Odum’s conjectures
(Christensen, 1995). However, two metrics, ascendancy (Ulanowicz, 1980, 1986)
and exergy (Jørgensen, 1986, 1988a, b, 1992) have been the most explored.
Exergy expresses the ‘useful’ energy which must be dissipated to sustain an
ecosystem, and which is embodied within the biomass resident within that
ecosystem. Ascendancy is a measure of the flows through and between the biomass
compartments scaled by the system’s complexity. Biomass within compartments will
accumulate or diminish as a consequence of net energy flow into and out of
compartments, and, conversely, the organisation inherent within the biomass within
any compartment will constrain energy or nutrient flow through that compartment.
Thus, exergy and ascendancy are both related to the organisational structure of
biomass within, and the information connections provided by flows through, the
compartments which comprise the ecosystem (Christensen, 1995). Exergy and
ascendancy are viewed as ‘goal functions’ of the system, aspects that are expected
to be optimised or maximised by system development.
If such measures are to be used to assess ecosystem health, then they should
respond in a dose-dependent fashion to known stress (Jørgensen, 1999; Zhang et
al., 2003; Zhang, Jørgensen & Mahler, 2004), or differ between systems of known
developmental stage (Wulff & Ulanowicz, 1989; Baird, McGlade & Ulanowicz, 1991;
Christensen, 1994, 1995). In his 1995 article, Christensen ranked the maturity of 41
ecosystems and compared his ranking with that derived from maturity measures
based on ascendancy and exergy. Maturity rank, assessed in terms of Odum’s
successional attributes, was strongly correlated with ascendancy. Exergy responded
primarily to biomass rather than maturity, which led Christensen to suggest that
alternative methods of calculating exergy might be more appropriate in future studies.
Subsequent inclusion of the genetic complexity of organisms at different trophic
levels within the exergy calculation has sought to correct this shortcoming
(Jørgensen 1997, 1999).
With such disparate assessment of ecosystem maturity arising from the modelderived system level metrics proposed by researchers, it is unsurprising that
conflicting results are frequently obtained when measures of this type are extended
to indicators of ecosystem health (Dalsgaard, Lightfoot & Christensen, 1995; Lu & Li,
2003; Xu et al., 2004). Despite these discrepancies, the concepts of ascendancy and
exergy have received considerable attention within the ecosystem modelling and
ecosystem health communities. In order to explore the potential of the systems
modelling approach for UK ecosystems, and to illustrate its application, it is
necessary to have a system which is well-documented and data-rich as well as one
whose health status is unambiguously known. Few UK systems meet these
requirements at present, a notable exception being the Ythan estuary, Scotland.
16
6.4
Mass-balance measures and ecosystem health: a case study of the Ythan
estuary, Aberdeenshire
The Ythan estuary, Aberdeenshire, Scotland, is one of the best documented
estuarine system in the world with respect to its trophic networks. Over the past 40
years, scientists at Aberdeen University’s Culterty Field Station have documented the
food web of the estuary in terms of its binary linkages (Hall & Raffaelli, 1991;
Huxham, Beaney & Raffaelli, 1996), trophic flows (Milne & Dunnet, 1972; Baird &
Milne, 1981) and interaction strengths (Raffaelli & Hall, 1993; Emmerson & Raffaelli,
2004). Over that period, the estuary has experienced nitrogen enrichment from the
agricultural hinterland leading to severe eutrophication symptoms, manifested as a 2to 3-fold increase in river nitrogen, increasingly heavy blooms of opportunistic green
macro-algae, degradation of benthic invertebrate populations and concomitant
changes in numbers of shorebirds (Raffaelli, Hull & Milne, 1989; Raffaelli et al.,
1999).
Detailed biological surveys carried out in the 1960’s (pre-eutrophic) and 1990’s (late
eutrophic), enabled parameterisation of Ecopath models for each of these periods
and allowed comparison of ascendancy and related measures at the two stages of
eutrophication. The Ythan food web comprises c.100 macro-species (Hall & Raffaelli,
1991), but many of these were excluded from the analyses because of their trivial
biomass and/or energy flows. The subset of species analysed is very similar to that
described by Baird & Milne (1981) and Baird & Ulanowicz (1993), but unlike these
previous publications, we have not aggregated these species into a further reduced
sub-set of functional groups. In addition, we have included an important consumer,
the brown shrimp Crangon crangon, omitted from those previous analyses. Finally,
the previous analyses confounded data collected from different stages of the
eutrophication period.
Our analyses (Raffaelli et al., 2005) revealed that ascendancy increased by 50% of
the pre-eutrophic value over the eutrophication period, with system throughput
changing by a similar factor. System throughput is a measure of the total of all the
flows and hence an expression of ecosystem ‘size’ (Kay, Graham & Ulanowicz,
1989). Both ascendancy and system throughput indices are consistent with the
increase in biomass of the macroalgae Enteromorpha and the invertebrates Hydrobia
ulva, Macoma balthica and Nereis diversicolor which, more than compensate for the
10-fold decline in Corophium volutator, one of the main prey of shorebirds. Because
ascendancy reports the average mutual information of the system (its complexity)
multiplied by system throughput (Ulanowicz, 1986), and the information measure of
the Ythan is very similar in the 1960s and 1990s (c. 1.17), ascendancy is driven
mainly by system throughput. In other words, the system’s basic food web structure,
composition and topology (its complexity) is similar for the two periods, with no taxa
going extinct, but the biomass and production of many elements is much higher
following eutrophication. Relative ascendancy (expressed as % of development
capacity, a natural limit for ascendancy) was very similar in the two periods (c. 26%)
indicating that the Ythan as a system was able to accommodate the large scale
changes in nutrient loading, primary production and invertebrate biomass. In this
sense, the Ythan eutrophication process is consistent with Ulanowicz’s (1986) view
that eutrophication can be described as any increase in system ascendancy due to
nutrient enrichment that causes a rise in total system throughput, which more than
compensates for any concomitant fall in the mutual information content.
Whilst the eutrophication process in the Ythan is consistent with expectations from
systems and network theory, this result serves to illustrate an ambiguity with the use
17
of whole-system metrics for assessing ecosystem health. Over the last 40 years, the
Ythan has displayed major changes in the populations of many species which were
dramatic enough to see the catchment designated as a Nitrate Vulnerable Zone
(NVZ) under the EC Nitrates Directive (Raffaelli, Hull & Milne, 1989; Raffaelli et al.,
1999; in press). The system modelling approach has confirmed that the overall flows
and biomasses have increased markedly, but system measures remain (relative to
one another) broadly unchanged and, in absolute terms, ascendancy increased
rather than declined. In other words, information on shifts in key and charismatic
species that are of importance to stakeholders and policy makers, is not necessarily
captured by system-level metrics. Indeed, it is possible that even a catastrophic
collapse of the food web through the loss of shorebirds which would reduce the
system’s information content markedly, and hence potentially also its ascendancy,
would be more than compensated for by enhanced algal growth and increased
system throughput. From a systems-level perspective the Ythan could be considered
resilient to a very significant external perturbation, nutrient enrichment. However,
eutrophication effects are markedly non-monotonic and non-linear and as Raffaelli,
Hull & Milne (1989) and Raffaelli et al. (1999) have pointed out, continued increases
in nutrient load and blooms of macro-algae are expected to lead to collapse of the
system.
Whilst mass-balance models may have potential for ecosystem health assessment,
they are extremely data intensive. Parameterisation of the Ythan model was possible
because of the large body of work carried out over a 40-year period on the food web.
Few ecosystems in the UK would be so data rich and constructing models of other
systems would almost certainly require extensive data estimation or extrapolation
from other studies such that confidence in the outputs is correspondingly reduced.
6.5
Model-based approaches to ecosystem management from ecological
economics, environmental economics and resource economics
System-level modelling is applied widely within resource, environmental and
ecological economics to study interlinked ecological and economic systems e.g.
Tschirhart (2000; 2002), Perrings (1998). Such models typically consider economic
influences on the behaviour of people and address the ecological consequences of
economically-driven management actions within the ecosystem. For example,
Perrings & Stern (2000) used the ability of the Botswana rangelands to support cattle
grazing in the face of the exogenous shocks caused by drought as a measure of the
underlying resilience of the rangeland ecosystem, and modelled the management
strategies of herd owners under prevailing economic conditions with a view to
devising economic incentives to promote resilience. Simonit & Perrings (2004)
modelled the interactions between the market for artificial fertilisers in Kenya and the
potential harvest of a commercial fish species in Lake Victoria, allowing for the
impact of nitrate run-off on algal blooms and fish growth.
The simplifications that are inevitably required to produce tractable models of
interacting ecological and economic systems can be problematic for adherents of the
single disciplines, but such simplifications are frequently justified for the following
reasons. Since the economic decisions of people exert considerable influence over
the majority of ecosystems on the planet, ignoring the impact of economically-driven
management action would present an incomplete and inadequate picture of
ecosystem status. Furthermore, economic data on, for example, prices, productive
outputs and revenues, are often readily available frequently as time series at monthly
or quarterly temporal resolution which extend over several tens of years. These data
might only be available at coarse (e.g. regional or national) spatial resolution, but can
have considerable predictive power when appropriate analytical techniques are
18
applied. For example, ARIMA models (Mills, 1990; Harvey, 1993) can capture
detailed trends within time series data which can enable quantified predictions to be
made for some distance into the future. Kalman filter models (Harvey, 1989) allow
the functional form of relationships between observed time series data and
underlying system variables to be estimated, and so can provide additional insight
into the status and function of managed ecosystems.
The difficulty with using time-series economic data of this type is that additional
linkages must be established within the model that allow the consequences of
economically-driven management action to be linked through to the target
ecosystem. For example, Simonit & Perrings’ (2004) model of Lake Victoria’s Nile
perch fishery required estimation of the linkage between fertiliser price and fertiliser
usage before shifts in fertiliser pricing could be used to predict changes in nitrate
runoff and their consequent impact on the fishery. Such linkages can be difficult to
establish, particularly in the agricultural sector where markets are often heavily
distorted by subsidies. Scenario-based approaches are therefore often applied to
models which feature economically-driven management actions to circumvent such
difficulties (e.g. Chalmers & Crabtree, 1999).
7
Application of different approaches for evaluating the health of terrestrial
and aquatic systems
7.1
Agroecosystem health
Xu and Mage’s (2001) definition of a healthy managed ecosystem as: ‘the
system’s ability to realise its functions desired by society and to maintain its structure
needed by both its functions and by society over time’, is especially applicable to
agroecosystems. Agroecosystems characterise over 70% of the European
landscape, and are managed directly by humans, making them fundamentally
different from unmanaged ecosystems (Piorr, 2003). Agroecosytems are designed
and managed primarily to produce food, fibre and other agricultural products for
human use (Waltner-Toews, 1994; Gallopin, 1995), and they also contain elements
of natural capital which are of value to society, such as biodiversity. Consequently,
agriculture has a multifunctional role spanning economic, social and ecological
dimensions (Waldhardt, Simmering & Albrecht, 2003). Historically, Europe was
characterised by patchy and highly structured rural landscapes which supported high
biodiversity (Piorr, 2003). However, the requirement to maximise crop production by
use of high-yielding crop varieties, fertilisation, irrigation and pesticides has resulted
in a drastic decline in biodiversity within agroecosystems, and has also affected
abiotic resources, such as water and soil (Cooper, 1993). In response, the EU has
initiated a number of measures to curb this decline and conserve biodiversity in
agricultural landscapes (Marggraf, 1998 cited in Waldhardt, Simmering & Albrecht,
2003; see also below).
7.1.1
Biological monitoring in agroecosystems
Monitoring is already undertaken in agricultural landscapes by both farmers and
governments, e.g. yields and area under cultivation. However, monitoring of this type
is undertaken primarily for short-term economic reasons and is of limited value to
long-term considerations of sustainability (Pannell & Glenn, 2000).
The EU Commission to the Council of European Parliament recognised the
complexity inherent in the use of indicators to monitor biodiversity within
19
agroecosystems and proposed a system-based approach which involved both
structural and functional components of the ecosystem. This approach uses
landscape-based indictors that cover more than one environmental theme so as to
incorporate interactions between agriculture, environment and socio-economic
conditions (Piorr, 2003). Van der Werf and Petit (2002) reviewed twelve different
agroecosystem evaluation methods that use indicator sets of this type (Table 7.1)
Table 7.1 Twelve different agroecosystem evaluation methods (after van der Werf &
Petit, 2002)
Method
Description
Evaluation
Farmer sustainability
index (FSI)
Assesses the production methods used by a
farmer to develop a FSI which reflects
ecological sustainability (Taylor et al., 1993)
Environmental
sustainability
Sustainability of
energy crops
Range of indicators mainly based on life cycle
analysis (Heijungs et al., 1992) used to assess
the ecological and economic sustainability of
growing and conversion of crops to energy
(Biewinga & van der Bijl, 1996)
Ecological and
economic
sustainability
Ecopoints (EP)
Method assigns scores to farm production
practices and landscape maintenance to
evaluate the extensification of land use and
quality of landscape management (Mayrhofer et
al., 1996)
Environmental
impact and
landscape quality
Life cycle analysis
for agriculture
(LCAA)
Environmental impacts of agricultural products
are evaluated by quantifying emissions, material
and energy use at all stages of their life cycle
(Audsley et al., 1997).
Resource use and
environmental
impact
Agro-ecological
indicators (AEI)
Environmental impact assessment method with
indicators used to evaluate the effects of farm
production practices on components of the
agroecosystem (Girardin, Bockstaller & van der
Werf, 2000)
Impact of practices
on
agroecosystems
and the
environment
Agro-ecological
system attributes
(AESA)
Framework for monitoring, modelling, analysing,
and comparing the state and performance of
integrated agroecosystems using quantitative
systems ecology. ECOPATH mass balance
modelling is used in this method (Dalsgaard &
Oficial, 1997)
Model, analyse
and quantify the
state and
performance of
agroecosystems
(Table 7.1 continues overleaf)
Table 7.1 contd.
Method
Description
Evaluation
Operationalising
sustainability (OS)
Sustainable farming systems are designed
using interactive multiple goal linear
programming to optimise environmental and
Environmental
sustainability
20
economic objectives at the farm level and also
consider socio-economic constraints (Rossing
et al., 1997)
Multi-objective
parameters
Method uses ‘multi-objective parameter’
indicators to design integrated and ecological
arable farming systems. It considers a set of
ecological, economic and social objectives
chosen to solve the problems cause by the
current farming system (Vereijken, 1997)
Environmental
sustainability
Environmental
management for
agriculture (EMA)
Computer-based environmental management
system, which compares actual farm production
practices and site-specific details with perceived
best practice for that site to produce eco-ratings
that reflect environmental performance (Lewis &
Bardon, 1998)
Environmental
performance
Solagro diagnosis
(SD)
Evaluation of the environment at the farm level
by considering the number of production
systems within the farm (crop and /or livestock),
diversity of crops grown, management of inputs
and management of space (Pointereau et al.,
1999)
Environmental
impact
Life cycle analysis
for environmental
farm management
(LCAE)
Emissions, material and energy use at all
stages of their life cycle are quantified to
produce a comprehensive evaluation of the
environmental impact of a farm. It identifies the
main pollution sources and evaluates possible
modifications of the farm or farming methods
(Rossier, 1999)
Environmental
impact
Indicators for farm
sustainability (IFS)
Ecological, social and economic sustainability of
farms is evaluated using indicators mainly
based on farm practices and farmer behaviour
(Vilain, 1999)
Ecological,
economic and
social sustainability
7.1.2
Indicators of biodiversity in agroecosystems
Many different indicators have been developed for use in agroecosystems (Buchs,
2003a), but few have been examined for their association with biodiversity (Duelli &
Obrist, 2003). Invertebrate monitoring has been shown to be more suitable for
biodiversity assessment in short-term studies, and vegetation monitoring for
biodiversity assessment in longer-term studies. Appropriate combination of both of
these methods is therefore important (Perner & Malt, 2003). Invertebrates have
many advantages over plant communities as biotic indicators (Nickel & Hildebrandt,
2003), but identification of invertebrates is labour intensive and specialised. Large
environmental monitoring programmes therefore generally avoid using invertebrates
as biodiversity indicators in order to reduce cost and effort (Duelli & Obrist, 2003).
21
7.1.3
Population and community ecological parameters as indicators of
change in agroecosystems
Several population and community-level ecological parameters have been proposed
as indicators to monitor gradual changes in agroecosystems (Table 7.2) (Buchs et
al., 2003). Species number, abundance and diversity are uncorrelated with the type
of farming system studied and therefore such metrics are not suitable indicators of
biodiversity in farming systems (Buchs et al., 2003). The shifting of dominance
positions in the species concerned has been found to be more important than the
loss of an individual species (Buchs et al., 2003). This change in dominance is
accompanied by a change in body size which may be a more appropriate indicator
(Duelli & Obrist, 2003). Body length, body weight, duration of activity period and other
parameters relating to on the fitness of the population influence the success of
reproduction and are therefore useful indictors that indirectly influence biodiversity
(Buchs et al., 2003). An important criterion for the assessment of the sustainability of
a farming system is the abundance of pioneer species in the overall species
community. Pioneer species occur in high densities and are euryoecious (they
possess low specific habitat requirements and can colonise heavily disturbed or
periodically newly arising areas). These types of species will thrive in disturbed
areas which are created within agroecosystems by processes such as harvesting
and pesticide usage, while other species, which are stenotopic (species closely
adapted to a particular environment) for example Lycosid butterflies, are very
sensitive to these interventions (Hammer, 1984; Stippich & Kroob, 1997 cited in
Buchs et al., 2003). Both types of species may be useful indicator species.
Table 7.2 Proposed ecological parameters to monitor changes in agroecosystems
(Buchs et al., 2003)
Population Level Ecological Parameters
Community Level Ecological Parameters
Activity, density, abundance
Presence, constancy, frequency
Period of activity
Body size
Body weight
Feeding rates
Growth rates
Age structure
Sex ratio
Egg production
Period/rate of reproduction
Morphological features
Biodiversity
Structural shaping of species communities
Similarity of species/dominance structure,
shifting dominance, correlations
Ratio of euryoecious/stenoecious species
Ratio of r- and K-strategists (see section 6.2)
Biotic/abiotic preferences
Predator-prey relationships
Rates of species turnover
Percentage of rare and/endangered species
Body size
Biomass
Morphological features
7.1.4
Means-based and effect-based indicators of environmental impact in
agroecosystems
The environmental impact of agriculture depends largely on the production methods
adopted by the farmer, although this link is indirect. Indicators have been developed
to measure production practices (means-based e.g. amount of nitrogen applied), or
the effect that these practices have on the environment (effect-based e.g. nitrate
level in the soil at harvest). Effect-based indicators are preferred because the
22
linkage is more direct. Whilst means-based indicators do not evaluate the
environmental directly, and validation is difficult, they are usually cheaper to
implement as they require less intensive data collection (van der Werf & Petit, 2002).
7.1.5
Direct and surrogate indicators in agroecosystems
Indicators can be categorised as direct or surrogate indicators (Buchs, 2003a). Direct
indicators are determined by direct measurement and provide information related to
management intensity or habitat conditions, for example, r- and K-selection
characteristics (defined in section 6.2); body size; body weight; taxonomic distance
(more sensitive to environmental change than diversity indices and does not rely on
sample size (Warwick & Clarke, 1998)); percentage of specialists and pioneer
species; stenotopic species (species closely adapted to certain biotic or abiotic
conditions); ratio of macropterous (having large wings) to brachypterous (having
short wings) species; invertebrate species communities related to ‘Ellenberg’indicator values (indicator developed for humidity by Ellenberg et al. (1992)); and
correlation of plant and animal assemblages.
Surrogate indicators provide an indirect measurement of agroecosystem health and
are currently used in many monitoring programmes, but they can produce misleading
results. The demand for simplified methods of biodiversity assessment means that
the development of surrogate indicators for biotic assessments is extremely
important. Future research needs to focus on how these surrogate indicators
correlate with quantitative (e.g. number of species) and qualitative (e.g. fitness)
features of agricultural landscapes (Buchs, 2003a). Examples of surrogate indicators
currently used within agroecosystems include: 1) an index of soil quality, which is
correlated with carabid species diversity; 2) length and quality of field margins; 3)
organic farming method, which contributes to nature protection but may not be an
ideal indicator for biodiversity (Doring & Kromp, 2003); and 4) landscape biodiversity
indicators relating to habitat features, that can predict the effects of hypothetical landuse changes.
7.1.6
Historical development of agroecosystem indicators in Europe
In 1998, the European Council highlighted the importance of agri-environmental
indicators for monitoring sustainable land use and biodiversity (Buchs, 2003b).
Subsequently, the Organisation for Economic Cooperation (OECD) developed a
range of agri-environmental indicators based on the Driving Force-State-Response
framework, which focused on the causes of change in the environmental conditions
within agriculture, the effects of agriculture on the environment, and the response
taken to changes in the environment (Buchs, 2003b; Piorr, 2003). These indicators
covered 13 different areas including biodiversity, wildlife habitats, landscape, farm
management, pesticide use, nutrient use, water use, soil quality, greenhouse gases,
socio-cultural issues and farm financial resources (OECD, 1999; 2000). In May 1999,
the European Commission proposed that more concrete indicators should be
developed to address land characteristics (e.g. patch density, edge density, Shannon
index), cultural features (e.g. number and status of point feature, length of linear
features, surface of areal features), and management features (e.g. area protected
related to total agricultural area) (Morad et al., 1999 cited in Piorr, 2003). Between
April 1998 and October 1999, the European Centre for Nature Conservation (ECNC)
coordinated and organised the EU Concerted Action project on agri-environmental
indicators. The project, named ELISA (Environmental Indicators for Sustainable
Agriculture) aims to identify key environmental issues, which are of policy concern
related to sustainable agriculture. This programme includes: selecting sets of core
agri-environmental indicators which provide an accurate measurement of sustainable
23
agriculture; developing a framework for these indicators which includes the current
state of the environment, driving forces behind agricultural practices and the
functional interrelationships between different components; and making practical
proposals for methods to gather, manage and interpret data at the appropriate
scales. Twenty-two state indicators and 12 pressure indicators were selected as
core agri-environmental indicator sets (Table 7.3).
Table 7.3 State and pressure indicators selected by ELISA (after Wascher, 2000)
Indicator Type
Theme
Indicator
State
Soil
Water erosion
Wind erosion
Soil compaction
Pesticides in soil
Water
Nitrates in river
Nitrates in groundwater
Nitrates in drinking water
Pesticides in groundwater
Pesticides in rivers/surface waters
Groundwater level
Biodiversity
Spatial complexity
Corridors and linkages between habitat types
Size or % of characteristic habitat types
Flagship species
Species richness
Species population trends
Genetic diversity in semi-natural agro-ecosystems
Genetic diversity in farm species
Landscape
Biophysical adequateness of land use
Openness versus closeness
Adequateness of key cultural features
Land recognized for its scenic or scientific value
Land use
intensity
Proportion of area irrigated
Yield of cereals
Proportion of farms with >50% cereals
Proportion of UAA in total area
Livestock density
Nutrients
N-discharge
Nitrate surplus
Pesticides
Direct usage data per pesticide
Sales data per pesticide
Pesticides cost per crop
Estimated usage data per crop
Pesticide risk
Driving Force
A recent initiative developed by the European Commission is the Proposal on AgriEnvironmental Indicators (PAIS). This aims to contribute to the development of agrienvironmental indicators by focussing on landscapes, agricultural practices and rural
development. These three indicator themes were chosen as they are at a very basic
24
conceptual stage compared to other indicators, and lack statistical data (LANDSIS
g.e.i.e. et al., 2000). After extensive research, 35 different landscape indicators, 23
different indicators relating to agricultural practices (eight of these indicators partly
overlap with those in the landscape section) (Table 7.4) and 55 different rural
development indicators were defined. These indicators were proposed on the basis
of national experience; the next stage of the PAIS is to determine their applicability
across the EU as a whole (LANDSIS g.e.i.e. et al., 2000).
Many countries outside Europe, for example Canada, USA, and Australia, have also
started to develop agri-environmental indicators. However, their choice of indicators
cannot necessarily be extrapolated to Europe due to different perceptions of
landscape and the strict division which is typically made between landscapes for
production and landscapes for nature conservation in North America and Australia, in
contrast to Europe (Buchs, 2003b).
Table 7.4 Indicators related to agricultural practice, proposed by the PAIS (LANDSIS
g.e.i.e. et al., 2000)
Indicator theme
Indicator name
Main agricultural land use type
Area of agricultural land
Arable land
Grassland
Wetlands
Scrubs
Cultivated crops
Acreage of cultivated crops
Yields of crops
Crop diversity
Farm management systems
Organic farming
Integrated plant cultivation
Farm management with environmental monitoring
Farm management according to good agricultural
practice
Extensification
Extensification of farmland by discontinuation of
farming
Extensification by introduction of extensive cultivation
methods
Soil protection
Soil cover by crops
Soil cover by stubble and mulch
Humus balance
Cultivation methods
Direct drilling
Tillage intensity
Irrigation
Water consumption
Irrigation technique
Field margins
Field margin cultivation
Hedgerow cultivation
25
7.1.7
Historical application of agroecosystem indicators within England
In October 2002, Defra launched the English Biodiversity Strategy which listed a
series of polices and objectives for protecting and enhancing biodiversity. A number
of sectors were identified as key for the protection of biodiversity, one of which was
agriculture. The strategy designated eight headline indicators for use to provide a
broad overview of current trends in biodiversity, and a further 36 indicators which
were specific to different sectors. In December 2003, Defra published an updated
set of indicators and provided a baseline assessment of biodiversity in England. This
indicator set will be further revised in 2006, and, by 2010, a time series for these
indicators will be published to provide an assessment of England’s contribution to
European and global targets for biodiversity (Defra, 2003).
Five of these biodiversity indicators have been developed specifically for agriculture
(population of farmland birds in England; condition of farmland SSSIs in England;
status of farmland Biodiversity Action Plan (BAP) priority species and habitats in
England; trends in plant diversity in fields and field margins in England; and extent
and condition of farmland habitat features in England). Another indicator, the area of
land under agri-environment agreement in England, is also applicable to the
assessment of biodiversity in agricultural systems.
7.2
Forest ecosystem health
Forests provide ecological, economic, aesthetic and cultural resources and functions,
which are essential to the Earth’s homeostasis (Ferretti, 1997). However, forest
ecosystems are currently threatened from a variety of factors including atmospheric
pollutants, global climate change, and pests and pathogens (Schlaepfer, 1993), as
well as unsustainable deforestation and harvesting of non-timber products. Since
forest ecosystems are usually extremely complex, assessment of forest ecosystem
health has been confounded by definitional difficulties of the type discussed
previously. As a consequence, many definitions of forest ecosystem health use
ambiguous or immeasurable terms and this has rendered the application of the
health concept in forest management impractical and difficult (Kolb, Wagner &
Covington, 1994). Definitions of forest health range from those which adopt a
utilitarian perspective to those which adopt an ecosystem perspective. Utilitarian
definitions of forest health focus on the ability of forest to provide for human needs;
for example ‘a condition where biotic and abiotic influences on forests do not threaten
management objectives now or in the future’ (USDA Forest Service, 1993). This
definition is focused solely on the outcome of management objectives and
ecosystems that meet these objectives are considered healthy while those that do
not are considered unhealthy. However, forest management objectives vary widely
and often produce conditions that may be viewed as healthy from one perspective
but unhealthy from another. This approach is therefore only appropriate where
unambiguous management objectives exist, for example in large industrial forests
used only for the production of wood fibre, but these situations are quite rare as most
forests have multiple uses and many interested parties are stakeholders in
management (Kolb, Wagner & Covington, 1994).
Definitions of forest health which adopt an ecological perspective focus on
ecosystem structures and functions, for example ‘A forest in good health is a fully
functioning community of plants and animals and their physical environment’ (Monnig
& Byler, 1992), ‘A healthy forest is an ecosystem in balance’ (Monnig & Byler, 1992),
and ‘A healthy forest is one that is resilient to changes’ (Joseph et al., 1991). These
definitions consider the ecological processes that create forest conditions that are
favourable to a range of objectives. However, these definitions use terms that are
26
difficult to measure (e.g. ‘fully functioning’, ‘in balance’, ‘resilient’), and often focus
purely on bio-physical aspects which omit the human dimension. Kolb, Wagner and
Covington (1994) state that any definition of forest health must incorporate the
processes, structures and resources required to produce a productive forest in terms
of criteria defined by society in general. They proposed that forest health could be
defined using four qualitative characteristics: 1) the physical environment, biotic
resources and trophic networks required to support productive forests during at least
some seral stages; 2) resistance to catastrophic change and/or the ability to recover
from catastrophic change at the landscape level; 3) a functional equilibrium between
supply and demand of essential resources for major portions of the vegetation; 4) a
diversity of seral stages and stand structures that provide habitat for many native
species and all essential ecosystem processes. These characteristics incorporate
considerations of temporal and spatial scales, which are extremely important within
forest ecosystems. Many species in forests follow cyclic patterns, prolonged
dormancy (Ferretti, 1997) and delayed effects caused by various stressors (Becker,
1989; Landmann, 1989; Gandolfo & Tessier, 1994). Succession is a natural process
resulting in the replacement of one species, or group of species with similar
ecological characteristics, by another and this turnover of species should not be
regarded as an indication of poor health. Assessments of forest health produce
different results depending on the spatial scale applied. Those aimed at the
individual level provide an assessment of individual tree health and cannot be
extrapolated across the whole forest ecosystem due to ecosystem complexity (Kolb,
Wagner & Covington, 1994). The Forest Service in America produced their own
definition of forest health which covered multiple spatial and temporal scales, and
both biophysical and human dimensions: ‘forest health is a condition wherein a forest
has the capacity across the landscape for renewal, for recovery from a wide range of
disturbances, and for retention of its ecological resiliency, while meeting current and
future needs of people for desired levels of values, uses, products, and services’
(Twery & Gottschalk, 1996). This definition has been extremely influential in forest
management decisions in the USA. Percy (2002) listed a number of forest attributes
which contribute to the overall health of a forest ecosystem (Table 7.5).
Table 7.5 Factors that contribute to forest health (after Percy, 2002)
Type of factor
Factor
Structural
Stand structure
Species’ life history
Genetic diversity
Soil quality
Site edaphics
Site history
Management practices
Processes
Net primary productivity
Biogeochemical cycles
Water cycles
Organic matter cycles
Insect population cycles
Disease incidence and severity
Growth environment
Solar radiation
Temperature
Precipitation
Growing season length
Air pollution
Extreme events
27
7.2.1
Monitoring forest ecosystems
Recent concern over the health of forests has initiated many assessment and
monitoring programmes worldwide (e.g. International Cooperative Programme on
Assessment and Monitoring of Air Pollution Effects on Forests, U.S. National Acid
Precipitation Assessment Program, Environmental Protection Agency Forest Health
Monitoring), and many of these have become an integral part of forest management.
In order to determine management plans and inform policy makers it is crucial to be
able to predict the responses of forest ecosystems to changes in environmental
conditions (Pylvänäinen, 1993) and this requires that appropriate data are collected
in monitoring studies. It is not usually possible to undertake experimental studies
which investigate the environmental drivers of change at the forest scale (SchmidHass, 1991), and since results from small-scale studies cannot be extrapolated
across a whole ecosystem (Kelly et al., 1995), monitoring programmes must cover
the whole ecosystem. Ideally, continuous long-term monitoring of the whole forest
ecosystem should be undertaken but this may be constrained by financial, scientific
and technological factors (Ferretti, 1997).
7.2.2
Indicators of forest health
A number of biotic and abiotic components and processes make up forest
ecosystems, but single, individual measurements of these do not provide an
adequate assessment of forest ecosystem health. It is therefore necessary to use a
composite of suitable indicators which cover ecological, economic, environmental
and social factors. A range of potential indicators have been developed which
address structural and functional attributes of a forest, for example plant community
structure, biodiversity, primary productivity, decomposition rates, interactions of
consumers/producers, and chemical balance (Ferretti, 1997). In Europe, the majority
of forest health assessments have been based on one indicator (trees) and two
indices (crown transparency and discolouration), but these methods entail a number
of problems in terms of basic assessment. For example, visual estimates are
subjective and it is therefore difficult to compare data from different surveyors (e.g.
Innes, 1988a, b; Skelly, 1993).
7.2.3
Indicators of forest health in Britain
The Forestry Commission has developed a large set of indicators for sustainable
forestry (UK Sustainable Forestry Indicators) covering woodlands, biodiversity,
condition of the forest and environment, timber and other forest products, people and
forests, and economic aspects (Table 7.6). The majority of these are currently being
assessed and, where possible, compared to historical data to detect trends (Forestry
Commission, 2002). Although these indicators have not been designed specifically
to measure forest health, a number of them could be used within a forest health
assessment.
Defra (2004) recommended specific indicators to assess the state of woodlands in
the UK within the ‘Landscape and Wildlife’ section of its ‘Quality of Life Counts’.
These include area of ancient woodland in the UK, area of ancient semi-natural
woodland in the UK, sustainable management of woodland, and the number of
countries with national forest programmes. Woodlands are an important feature of
the landscape in Great Britain and many environmental policies have been
developed recently which aim to maintain and enhance the environmental
characteristics of woodlands and the contribution that they make to the countryside
(Countryside Survey, 2000).
28
Table 7.6 Indicators designed and used by the UK Forestry Commission (Forestry
Commission, 2002).
Indicator Group
Indicator
Woodland
Woodland area
New woodland creation (and type)
Loss of woodland (and type)
Tree species
Woodlands in landscape
Area of sustainably managed woodland
Management practices
Biodiversity
Ancient woodland
Native woodland area
Native woodland condition
Abundance of fauna
Richness of flora
Diversity of woodland within a stand
Natural regeneration of woodland
Condition of forest and environment
Air pollutant
Soil chemistry
Water quality
Surface water acidification
Water yield and stream flows
River habitat quality
Pollution incidents
Crown density
Damage by living organisms
Other damage (wind and fire)
Timber and other forest products
Volume of growing stock
Harvesting compared with annual increment
Timber production and future availability
Home-grown timber as % of consumption
Carbon storage
People and forests
Visits to woodland
Extent of open public access
Public awareness
Community involvement
Historic environment and cultural heritage
Health & safety
Economic aspects
Financial return from forestry
Value added in forestry
Value added in wood processing
Employment
Social and environmental benefits
7.3
Aquatic ecosystem health
The health of an aquatic ecosystem may be defined as the degree to which the
physical and chemical attributes of the system, its biota and their habitats, match the
natural conditions at all spatial and temporal scales (Karr, 1991). Assessments of
aquatic health must, however, also include human values which reflect the uses and
amenities derived from the system (Rapport 1989; 1995; and others within Boulton,
1999). An assessment of aquatic ecosystem health should inform society about the
29
conditions of rivers and their catchments, and also about the quality of life of the
people living in the riparian zone (Karr, 1999).
Ladson and White (2000) defined seven components of an aquatic system which
contributed to its overall health. These were: water quality (e.g. amount of nutrients,
turbidy and pollutants in the water); physical habitat (e.g. cover for fish, presence of
logs); riparian quality (e.g. extent of riparian zone, type of species present); aquatic
biology (e.g. type and abundance of macroinvertebrates and fish); physical form (e.g.
erosion and sedimentation); aesthetics (e.g. appearance); and hydrology (e.g. flow,
velocity, flood frequency and magnitude). A holistic approach would consider all of
these components when assessing the health of an aquatic environment.
Early studies of aquatic systems used the abundance or presence of individual
indicator species as a measure of health status, but community level analyses have
become prevalent since the 1970s. Community-level methods incorporate community
composition and overall system metrics (e.g. exergy and ecological buffering
capacities (Xu et al., 2001)) together with measures of ecosystem structure (e.g. cell
size in phytoplankton, body size in zooplankton) and function (e.g. rate of carbon
assimilation by algae, ratio of production to respiration across the community (Yan &
Strus, 1980; Odum, 1985; Schindler, 1990; Havens, 1992)) to provide a more
comprehensive assessment of ecosystem health (Schindler, 1987; Baudo, Rossi &
Quevauviller, 1995; Richards, Johnson & Host, 1996; Bain et al., 2000) (also see
Table 7.7). Physical and chemical metrics are also used widely in addition to purely
biotic measures. The following sections describe commonly used metrics from all of
these categories.
Table 7.7 Ecological indicators used to assess structural, functional and system-level
determinants of ecosystem health (after Xu et al. 2001)
Type of indicator
Ecological indicator
Indication of health
Structural
Phytoplankton cell size
Zooplankton body size
Phytoplankton biomass
Zooplankton biomass
Macrozooplankton biomass
Microzooplankton biomass
Phyto-/zooplankton biomass ratio
Macro-/microzooplankton biomass ratio
Species diversity
Small
Large
Low
High
High
Low
High
High
High
Functional
Algal Carbon assimilation ratio
Resource use efficiency
Community production
P/R ratio*
P/B ratio*
B/E ratio*
High
High
Low
~1
High
High
System-level
Buffer capacities
Exergy (defined in section 6.3)
Structural exergy (defined in section 6.3)
High
High
High
* P/R is the ratio of gross production to community respiration; P/B is the ratio of
gross production to standing crop biomass; B/E is the ratio of standing crop biomass
to unit energy flow (see Xu et al. 2001 for further details)
30
7.3.1
Physical and chemical indicators of aquatic ecosystem health
Aquatic biota are dependent on their physical and chemical environment, so it is
unsurprising that physical and chemical indicators are used widely to monitor water
quality in aquatic health assessment. Physical and chemical indicators are often very
specific, being designed to detect single chemicals or particular conditions. They
usually only provide snapshot values and generally do not integrate information
across spatial or temporal scales (Harris & Silveira, 1999). Furthermore, the
reference values used can fail to account for natural variation in water chemistry
(Norris & Thoms, 1999). A more comprehensive picture can be achieved by
incorporating biotic indicators together with physical and chemical indicators within
the health assessment (Harris & Silveira, 1999). The concentrations of the following
chemical and biological elements and compounds are regularly monitored to provide
an indication of water condition: dissolved oxygen, ammonia, nitrate, orthophosphate, E.coli and cholorophyll-a (Ladson & White, 2000). Geomorphological
indicators can also be used to assess aquatic health, but distinguishing between
natural and human-induced changes in the physical characteristics of rivers can be
challenging.
7.3.2
Biotic indicators of aquatic ecosystem health
The composition, structure and function of biological communities are monitored to
detect changes in the health of aquatic ecosystems (Roy & Hanninen, 1995).
Structural measures have been shown to be more sensitive to stress than functional
measures (Schindler, 1987; Pratt, 1990), and, consequently, many biotic indicators
focus on community structure. Measures are typically based on presence/absence of
indicator species and estimation or quantification of deleterious effects on particular
organisms (Baudo, Rossi & Quevauviller, 1995). Reliance on a single indicator
species is undesirable, and so monitoring usually covers a range of species (Mason,
2002). A number of different metrics may be recorded across various taxa and then
combined to yield a multimetric biotic index of aquatic ecosystem health.
Benthic macroinvertebrates (Resh & Jackson, 1993; Boulton, 1999), fish (Harris,
1995), macrophytes (Whitton & Kelly, 1995) and algae (Brook, 1994) have all been
used as biotic indicators in aquatic systems. Macrophytes (Ali, Murphy & Abernethy,
1999) and algae are commonly used to monitor water quality, especially where
eutrophication is an issue (Brook, 1994; Kelly & Whitton, 1995; Kelly, 1998; Danilov &
Ekelund, 2000). Species richness, species abundance, condition and trophic
composition of these taxa are the metrics which are commonly monitored (Karr,
1991; Norris & Thoms, 1999). Bacteria offer the potential to provide excellent earlywarning signs of pollution. Sophisticated techniques are required for bacteriological
analysis, but developments in this area are proceeding rapidly (Mason, 2002).
Indices of biotic diversity are also used to monitor environmental stress as species
richness tends to decline, whilst the relative abundance of pollution tolerant species
tends to increase, when water quality deteriorates (Mason, 2002).
7.3.3
Multimetric biotic indices
A large number of multimetric biotic indices have been developed to assess water
quality (Metcalfe-Smith, 1996). A multimetric biotic index determines the sensitivity or
tolerance of species/groups to pollution from prior knowledge, determines the
abundance or richness for those groups at a site, and then produces an index of
health for the site from the observed abundances/richness metrics weighted by the
pollution tolerance of the species/groups concerned. Most multimetric biotic indices
have been developed to monitor organic pollution specifically and may therefore be
31
unsuitable for detecting other forms of pollution. The following multimetric biotic
indices are in widespread use.
7.3.3.1 Saprobic Index
The saprobic index considers the presence or absence of indicator species within the
four stages of oxidation of organic matter (oligosaprobic, alpha-mesosaprobic; betamesosaprobic and polysaprobic). It is widely used in continental Europe, less so in
Britain and North America (Mason, 2002).
7.3.3.2 Index of Biotic Integrity (IBI)
The IBI combines at least seven separate biological metrics from a particular
biological assemblage to produce an IBI index for a particular site. Metrics vary
depending on the assemblage monitored, e.g. abundance, richness, trophic
composition and body condition are typically recorded for fish communities. The IBI
provides an overall measure of the health of the community monitored (Mason,
2002).
7.3.3.3 Biological Monitoring Working Party (BMWP)
The BMWP combines taxon richness with a knowledge of a taxon’s sensitivity to
pollution to produce a simple measure of aquatic health. The BMWP is influenced by
sample size and an average pollution tolerance score per taxon monitored (ASPT)
value is often calculated for inter-site comparison (Mason, 2002).
7.3.4
Methods used to assess aquatic ecosystem health in Britain
Defra recommended a group of indicators within its ‘Quality of Life Counts’ strategy
for assessing the quality of freshwater resources against ecological and socioeconomic criteria (Defra, 1999). These include: river water quality (chemical and
biological); nutrient content (phosphate and nitrate); water demand; water availability;
water affordability; water leakage; purpose of abstraction; and number of sites
affected by water abstraction. Other sections of the strategy include a number of
relevant indicators such as: emissions of hazardous waste; household water use;
peak water demand; acidification; and estuarine water quality (Defra, 1999). These
indicators have been measured since 1970 and the current trends show a broad
variety of change (Defra, 2004).
The biological condition of watercourses was reported in the Countryside Survey
(2000) using a BMWP score generated with the assistance of the RIVPACS software
package. The method scored 82 different groups of aquatic macroinvertebrates in
terms of their resistance to pollution; pollution tolerant species being allotted low
scores. A BMWP score for a site was produced by summing the scores of all species
present, a high BMWP score indicating an unpolluted site. An ASPT (average score
per taxon) score was also calculated for each site (see section 7.3.3.3). Using
knowledge of the freshwater habitat type(s) present, the RIVPACS software
predicted the taxon richness and ASPT scores which would have been expected if
the surveyed site had been free from pollution. The current biological condition of the
site was then determined by the ratio of the observed and ‘unpolluted’ ASPT scores.
Sites across the country were assigned into one of five or six grades of biological
condition on this basis.
A River Habitat Survey which evaluated the physical structure of the watercourse in
terms of its in-stream and riparian features was also carried out within the
32
Countryside Survey (2000). A habitat quality assessment (HQA) and a habitat
modification score (HMS) result were produced. The HQA reported structural
diversity within the habitat, and the HMS reported the extent to which the habitat had
been subject to anthropogenic modification.
8
8.1
Interdisciplinary indicators of ecosystem health
The HEHI approach
As previously stated, many of the definitions and assessments of ecosystem health
concentrate solely on biophysical aspects of the ecosystem. It is, however,
becoming increasingly recognised that the overall health of an ecosystem relies on a
number of interrelated social, economic and ecological components. This highlights
the importance of using an holistic approach, which accommodates each of these
components and addresses their interdependence, when assessing ecosystem
health. Consequently, interdisciplinary indicators of ecosystem health are now being
proposed which incorporate socio-economic, ecological, and community
development components (e.g. Hannon, 1992; Costanza, 1994; Cobb, Halstead &
Rowe, 1995).
The Holistic Ecosystem Health Indicator (HEHI) (Aguilar, 1999) is an example of
such interdisciplinary index. HEHI incorporates ecological, social and interactive
(interactions between human and ecological components) indicators to provide a
more comprehensive assessment of the health of an ecosystem (Aguilar, 1999).
Each of the three components (ecological, social and interactive) are subdivided into
categories depending on the ecological and social characteristics of the target area,
and the management goals of the stakeholders involved (Aguilar, 1999). The
ecological component focuses on biophysical aspects of the ecosystem, particularly
organisation, vigour and resilience (sensu Costanza, 1992; see section 4). The
social component covers a range of socio-economic factors that are fundamental to
the exploitation of ecosystem resources (Winograd, 1995), and the indicators chosen
within this category reflect the social and economic priorities of the communities
which live in, or depend on, the ecosystem (Aguilar, 1999). The interactive category
quantifies the primary connections and relationships between people and the
ecosystem, as well as the effectiveness of regulatory agencies in implementing
legislation, community perceptions, awareness and involvement in management
decisions (Aguilar, 1999).
Specific indicators are selected to evaluate the condition of each of the three
components, and ideally a benchmark is set for each of the indicators based on
scientific literature or management objectives and policy. A standardised scoring
system is used to evaluate the individual indicators with higher scores representing
healthier ecosystems, and each indicator category is given a relative weighting
depending on its importance to the overall health of the ecosystem and stakeholder
goals (Munoz-Erickson & Aguilar-Gonzalez, 2003). This approach was originally
applied to tropical ecosystems in Costa Rica using nine ecological, six social, and six
interactive indicator categories (Fig. 8.1). In the Costa Rican study the ecological
component was weighted at 40% and both the other components at 30%. Restricted
availability of the necessary information only allowed a ‘weak’ health assessment to
be produced as several indicators were lacking within each component (Aguilar,
1999). HEHI was used in the Costa Rican situation to assess the health of the
ecosystem at a single point in time, but it would be more informative for policy
makers and managers to incorporate temporal trends into holistic approaches like
33
HEHI in order to identify the rate and direction of any changes, as done for a novel
method described below (MEHTA).
The main advantage of the HEHI approach is that it uses a simple and cost-effective
methodology that allows managers and policy makers to focus their resources on the
weakest aspects of ecosystem health. It also permits comparisons between different
sites, and can thus reflect general trends at global, regional and local scales.
Identifying appropriate indicators within each component, meaningful benchmarks for
those indicators and a realistic time scale for assessment is, however, potentially
difficult (Munoz-Erickson, Loeser & Aguilar-Gonzalez, 2004). Value judgements
have to be made, (and defended), concerning the relative weightings of the three
main components as well as in the directionality of the association between each of
the indicators and ecosystem health. Ideally, these judgements are best made by
local communities within existing regulatory and statutory frameworks. A drawback
with composite indices such as HEHI is that they cannot unambiguously identify the
underlying causes of changes in the health status of an ecosystem (Aguilar, 1999).
Holistic Ecosystem Health Indicator
(HEHI)
Ecological
Soil quality
Riparian zone
Water quality
Biomass
Land use
Primary productivity
Regeneration
Biodiversity
Erosion
Social
Income
Access to services
Job stability
Gender roles
Demographics
Community strength
Interactive
Land use and distribution
Watershed protection
Land degradation
Citizen involvement
Implementation of legislation
Environmental awareness
Fig 8.1 Structure of the Holistic Ecosystem Health Indicator (HEHI) used by Aguilar
(1999) in tropical managed systems in Costa Rica.
8.2
MEHTA (Monitoring of Ecosystem Health by Trends Analysis): an
alternative interdisciplinary indicator
In this section we take the holistic indicator approach a stage further. Specifically, we
illustrate how the HEHI-type approach can be developed by incorporating a strong
temporal component, ideally incorporating historical data, to assess the rate of
change in individual indicators and their directionality with respect to targets or
thresholds established by a combination of statutory requirements and stakeholder
consultation. In addition, the choice of indicators has a sound theory basis and the
behaviour of the indicators is therefore easier to appreciate and understand. The
method (MEHTA:Monitoring of Ecosystem Health by Trends Analysis) could not be
developed fully during the life of the current project, so that here we only illustrate the
potential of the approach as well as identifying knowledge gaps which need to be
addressed if this and other holistic indicator approaches are to fully realised.
34
Nevertheless, MEHTA is actively under development at the University of York
Raffaelli et al, in prep., White et al in prep, Smart et al, in prep), and reference to it
should not be made without the permission of the authors.
Our guiding principle in reviewing the literature and techniques available for
assessing ecosystem health has been that a good indicator of ecosystem health
should represent a measure of social welfare or utility (section 2.2), and thus the
maintenance of healthy ecosystems is a prerequisite for sustainable development. As
described in section 2.2, a healthy ecosystem is one in which sufficient stocks of
natural capital are maintained to support the ecosystem services which flow from
those stocks and which, in combination with social, human and financial capital,
deliver marketed and non-marketed products of socio-economic value.
Natural capital can take a variety of forms. The thematic categorisation used in the
ELISA programme (see Table 7.3) proves helpful in identifying broad environmental
(soil-based and water-based) and ecological (biodiversity-based) components of
natural capital within ecosystems. Certain products of value to society arise from
stocks of environmental and ecological capital with minimal human intervention (e.g.
rare or charismatic species and their associated existence values, clean air or water
delivered by the natural purification services afforded by forests and woodlands),
system resilience to invasive species and disease. Other products of socio-economic
relevance arise only when ecosystem services underpinned by stocks of ecological
and environmental capital are combined with stocks of man-made capital (financial,
human or social capital) through active management of the ecosystem concerned.
For example, agricultural crops are produced by combining the pollination and soil
fertility services which are supported by ecological and environmental capital with
human, financial and social capital in the form of agricultural labour, investment in
seed and equipment provision and the sales and marketing infrastructure of agribusiness. Thus, in order to ensure that managed ecosystems continue to deliver a
desired bundle of products of relevance and value to society, adequate stocks of
environmental, ecological, financial, human and social capital must be maintained
within those managed ecosystems.
The health status of a managed ecosystem could therefore be assessed by
monitoring the status of the environmental, ecological, financial, human and social
capital stocks associated with that ecosystem, relative to critical thresholds for those
stocks which are necessary the maintain delivery of a desired bundle of products.
The MEHTA approach uses an appropriate set of indicators to report on the status of
these underlying capital stocks, assesses the safety margins which remain before the
critical thresholds for each stock are infringed, and derives a measure of the rate at
which capital stocks are being depleted or enhanced by utilising time series data for
the individual indicators concerned. Weightings can then be applied to the safety
margin and trend results for indicators of each element of capital stock to produce an
overall assessment of ecosystem health.
Weightings are produced by determining the relative socio-economic value attached
to products derived from the different capital stocks. These relative valuations are
ideally elicited from the social community which is part of the ecosystem concerned.
The MEHTA approach thus incorporates stakeholder knowledge and preferences to
produce a relative valuation of products derived from the ecosystem. Expert
knowledge can then be applied to determine those elements of natural and manmade capital which support product delivery and to establish critical thresholds for
the capital stocks concerned. This approach to the assessment of ecosystem health
incorporates the values and aspirations of society together with expert knowledge of
35
ecosystem structure and function, and thereby yields a health assessment which can
be regarded as a prerequisite for sustainable development.
Techniques such as portfolio analysis
(Markowitz, 1952, 1959; Alexander &
Francis, 1986; Acutt, 2002) could be used to identify a set or portfolio of capital
stocks required within a particular ecosystem to ensure continued delivery of a
bundle of products at minimum risk, (risk here being the risk of a failure in product
delivery). Indicators of the status of those stocks would be selected using specialist
knowledge of the ecosystem in question together with knowledge of the range of data
which were readily available. Indicators identified as representative of underlying
environmental, ecological, human and social capital stocks in the ELISA (Table 7.3)
and forest health (Table 7.6) frameworks could prove useful in this regard.
Assessment of the health of the ecosystem concerned could then proceed through
analysis of these indicator data in the manner described in the following section.
8.2.1
Analysing indicator data
Indicator data are generally available as time series measurements, detailing, for
example, nitrate levels in river water, number of visitors to an area, abundance of a
particular bird species, or average income per household. The advantage of utilising
time series data is that statistically significant trends can be identified and quantified
which allows the rate of approach to prescribed critical thresholds to be estimated
(Fig 8.2). A health weighting can be assigned to the trend and safety margin results
for each indicator based on the functional importance of the capital stock concerned
and the relative valuations placed by society on those products. The health
implications for each element of capital stock within the overall health assessment
are scaled by the weighting factor for that indicator to produce a health score. Scores
can then either be summed across the individual indicators to produce a composite
health score for the ecosystem as a whole or, perhaps more informatively, presented
as a series of individual trends.
36
Indicator 1
Indicator Data
Trend
Safety Margin
critical
threshold
Indicator 2
t
critical
threshold
Indicator 3
t
Indicator 4
critical
threshold
t
critical
threshold
critical
threshold
t
Fig. 8.2 Assessing ecosystem health by determining trends and safety margins for
individual indicators of the status of environmental, ecological, financial, human and
social capital stocks within a managed ecosystem. An illustration of the MEHTA
(Monitoring of Ecosystem Health by Trends Analysis) approach.
8.3
Contrasts with the HEHI approach
The MEHTA approach has some parallels with HEHI (Aguilar, 1999), but there are
two important differences. First, MEHTA indicators reflect the status of an essential
set of environmental, ecological, financial, human and social capital stocks which
underpin provision of a desired bundle of products within the managed ecosystem.
In this sense, they are derived from first principles. Second, the analysis utilises
historical time series data to determine the rate of approach to critical thresholds
associated with indicators of separate elements of capital stock, which enables the
(statistical) uncertainty surrounding these trends and safety margins to be quantified.
The latter feature should also permit quantification of any uncertainty surrounding
future predictions of ecosystem status.
As indicated above, a full development of the MEHTA approach was not possible
within the time frame of the present contract. Here, we illustrate the potential and
limitations of the MEHTA approach by exploring its application to two well-
37
documented but contrasting systems: the North York Moors National Park and the
catchment of the River Ythan in Aberdeenshire. The North York Moors was selected
because it is an example of an upland system which has a diversity of pressures and
constitutes a large geopolitical unit for which many types of data are specifically
collected. The Ythan is a lowland Scottish catchment, 90% of which is under
agriculture and where the pressures and drivers of ecosystem change are extremely
well-documented and understood. It should be noted that it was not our intention to
provide a definitive statement about the health of either the North York Moors or the
Ythan catchment systems, but rather to show how the MEHTA approach might be
usefully applied. A full assessment of these two systems would require more
extensive data sets and the development with stakeholders of acceptable thresholds
and limits, both of which were outwith the scope of the present project.
9
How the MEHTA approach could be applied
9.1
Case Study 1: The North York Moors National Park
The North York Moors National Park (NYMNP) covers an area of 1436 km 2 at
altitudes between sea level and 454m in North Yorkshire in the north of England. The
Park comprises three main landscape types: heather moorland (34%), agricultural
land (42%), and commercial forestry plantations and woodland (22%). This case
study pursued the following objectives:

To provide a concrete illustration of the application of the MEHTA method of
ecosystem health assessment.

To identify practical challenges in applying the MEHTA approach, particularly
those which result from lack of data and/or incompatibilities in temporal or
spatial resolution and coverage in currently available data sources.
Because of the limited timescale available, it was not possible to consult
stakeholders to determine the relative values ascribed to a range of products whose
delivery is supported by stocks of natural and man-made capital within managed
ecosystems in the NYMNP. However, to provide an example application of the
MEHTA method, the following capital stocks were regarded as central to the
continued delivery of a wide range of products of relevance to the resident
community

Environmental capital – in the form of the water purification capacity provided by
river catchments within the NYMNP.

Ecological capital – in the form of biodiversity, which supports ecosystem
services such as soil fertility, natural pest control and pollination which underpin
delivery of agricultural and forestry crops, and also maintains landscape features
which attract tourists and visitors to the NYMNP.

Financial capital – in the form of financial reserves available to land management
businesses within the NYMNP.

Human and social capital – in the form of an appropriately skilled labour force
accessible to land management businesses, viable rural communities and
functional rural infrastructure for production and dissemination of products
generated by land management businesses, including businesses which service
tourism.
38
Indicators of each of these capital stocks were identified from available data as
described below. A full MEHTA analysis would utilise specialist knowledge to
establish the associations between the bundle of products of socio-economic value
and the different capital stocks which underpin product delivery. Critical thresholds
for individual indicators of the capital stocks could then be established, possibly by
using a technique such as portfolio analysis to identify a portfolio of capital stocks
which would minimise the risk of a failure in product delivery. Here again, because of
the limited timescale available for this work, formal portfolio analysis has not yet been
performed, and critical thresholds for individual indicators have not yet been
established. Trend analysis of time series data for relevant indicators of the
underlying capital stocks has, however, been undertaken. The indicators used, the
analytical techniques applied and the results obtained are described in sections
11.1.1 and 11.1.2 following.
9.1.1
Indicators of natural and man-made capital stocks
Data at appropriate temporal and spatial scales proved difficult to obtain. Surrogate
indicators were used in some cases where data on more direct indicators were not
available. The following subsections describe the indicators used and suggests
where other data, which should in principle exist within current data collection
frameworks, could be used to strengthen the analysis.
9.1.1.1 Indicators of financial capital available to land management businesses
Revenues generated by land-based industries in the NYMNP were regarded as
indirect indicators of the stock of financial capital available to land management
businesses for combination with stocks of environmental and ecological capital to
yield products of socio-economic value. The four main sources of land-based
revenue generation within the NYMNP were considered: tourism, farming, forestry
and shooting (grouse). The number of visitor days within the whole of the NYMNP
was used as a surrogate measure of the total income generated from tourism
(NYMNP Education Service, 2001). Farming is the dominant land use category
within the NYMNP, and although it is recognised that the profit generated by different
farming sectors will vary, only data on the profit generated from hill sheep flocks were
available at the time of writing and these were therefore used as an indicator of the
profitability of farming in the NYMNP in general (Lewis, 2001). Most of the moorland
within the Park is owned by private estates, the majority of which run a grouse shoot
each year. Grouse shooting thus makes a significant contribution to the local
economy (Kirby, 2000). The total profit from grouse shooting is determined by the
number of grouse shot, their value and the cost of production. The number of grouse
bags per year from a representative sample of estates (n=8) was used as a
surrogate for the total profit generated from grouse shooting (after Kirby, 2000).
Details of the revenue generated within the forestry industry in the NYMNP could not
be obtained within the timescale of the contract, but these data exist and could in
principle be used in such analyses.
9.1.1.2 Indicators of ecological capital
Birds have been recognised as good indicators of the general state of wildlife and the
countryside as they have a broad habitat distribution and are at the top end of the
food chain (Defra, 1999). An index of the abundance of breeding birds (British Trust
for Ornithology (BTO) 2004) was therefore used as an indicator of biodiversity, a
stock of ecological capital which supports resilient delivery of agricultural and forestry
products. The BTO breeding bird data were available at a regional scale (North
Yorkshire), rather than for the NYMNP specifically. These regional data were
39
assumed representative of the status of breeding birds within the NYMNP. For a full
evaluation of the ecological capital stocks, other bird and other wildlife data should be
included, but the BTO data were the most readily available at the time and illustrate
the principle effectively.
9.1.1.3 Indicators of environmental capital
River water quality was used as a measure of the health of the aquatic environment
and the level of land contamination, both of which relate to water purification
capacity, an underlying stock of environmental capital. The mean nitrate content in
the four main rivers which rise in the NYMNP (Derwent, Rye, Esk and Seven) was
used as an indicator of river quality (Environment Agency, 2002).
Other
environmental quality variables could be used in addition to nitrate if a full
assessment of the health of this ecosystem was required, but for illustrative purposes
nitrate was deemed sufficient. Data detailing expenditure on fertilisers and pesticides
within the Park would provide a more direct measure of the level of land
contamination, and could potentially be obtained from the Farm Business Survey.
9.1.1.4 Indicators of human and social capital
The total population and the average (asking) house price within the park boundaries
were both used as indicators of the desirability of living in the Park and thus as
indicators of the stock of human and social capital present within the Park community
(NYMNP Authority, un-dated; NYMNP, 2003). Employment level also provides an
indication of social well-being, but these data were not available over a suitable time
period for inclusion in this analysis. The number of people employed in agriculture
was readily available, however, and these data were included (NYMNP Education
Service, un-dated). It is recognised that total employment in farming is only a subset
of the total employment within the NYMNP, but it also reflects the changing status of
farming. It may at first sight seem counter-intuitive that human population density is
used here as a positive indicator of the health of the system, given that
environmental degradation is largely driven by human behaviour. However, the more
attractive an area is perceived to be, the more it is likely to be sought as somewhere
to live. Similarly, people move away from degraded and unproductive rural areas.
Nevertheless, the functional form of the relationship between human density
behaviour and natural capital is likely to be complex and requires further
investigation.
9.1.2
Analysis and results
Critical thresholds could not be assigned within the time scale and resources of the
project for indicators of the separate capital stocks (see section 9.1). Here, we
illustrate the trend portions in the MEHTA health assessment scheme in the form of
regression-based trend analysis (Table 9.1).
Simple and multiple linear regressions were performed on the time series data sets
for each indicator using SPSS (v11). The data were checked for outliers and
influential cases using standardised residuals and Cook’s distance, respectively.
Explanatory variables were introduced sequentially (manually stepwise) into the
multiple linear regressions and only those variables that significantly improved the fit
of the model were retained. Harmonic terms were included in the regressions
applied to the grouse bag data to capture known cyclic variations in grouse
populations. The trend results obtained from the regression analyses for individual
indicators are shown in Table 9.1. Confidence intervals (95%) surrounding individual
predictions were determined for each data set (an example is provided in Fig. 9.1).
40
41
Table 9.1 Summary of trends in indicators of natural and man-made capital stocks in managed ecosystems within the NYMNP
Capital Stock
Indicator
Time span
Model Fit
R2
Adj. R2
Financial
(revenue
generation)
Visitor days
1992-2000
F1,7=85.708
0.924
0.887
Hill-sheep income
1994-2000
0.390
-0.043
Grouse
1968-1999
0.607
0.420
Ecological
(biodiversity)
Breeding bird (NY)
1994-2003 (exc.2001)
0.499
0.258
Environmental
(water
purification
capability)
Mean N2 (Rye)
1995, 2000-2002
0.468
-0.995
Mean N2 (Derwent)
1995, 2000-2002
0.992
0.970
Mean N2 (Seven)
1995, 2000-2002
0.803
0.261
Mean N2 (Esk)
1990, 1995, 2000-2002
0.287
-0.711
Farm employment
1966,1977,1986,1991,1996
0.972
0.798
Population
1966,1971,1981,1991,1997,2001
0.95
0.806
Average house
prices
1991-2003 (exc.1993)
0.578(lin)
0.901(exp)
0.277(lin)
0.827(exp)
Social & Human
(viable rural
communities)
1
Stein’s adjusted R2
P≤0.001
F1,5=3.196
P=0.134
F5,26=8.047
P≤0.001
F1,7=6.961
1
P=0.034
F1,2=1.759
P=0.316
F1,2=254.89
P=0.004
F1,2=8.147
P=0.010
F1,3=1.209
P=0.352
F2,2=34.461
P=0.028
F2,3=28.783
P=0.011
Linear
Exp.
F1,5=6.852 F1,5=45.49
P=0.047 P=0.001
Trend
42
12
Visitor days (million)
10
8
6
Visitor days (million)
4
predicted trend
low er individual 95% CI
upper individual 95% CI
2
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Year
Fig. 9.1 Actual and predicted number of visitor days (million) within the NYMNP
showing 95% upper and lower confidence intervals for individual predictions.
9.1.2.1
Revenues from land-based industries as indicators of financial capital
stocks within the NYMNP
The number of visitor days within the Park increased significantly between 19922000, suggesting that the revenue generated from tourism will have increased over
the same period. The total income from hill-sheep farming within the park declined
(although not significantly) between 1994-2000. Three key factors determine the
profitability of hill sheep farms: lamb production, subsidy payments and the costs of
maintaining the flock (Lewis, 2001). Costs were relatively stable throughout the
period studied but profit margins reduced in the late 1990s as a result of the fall in
value of lambs and breeding ewes, forcing farmers to become increasingly reliant on
subsidies (Lewis, 2001). Grouse numbers within the sampled estates followed eight
and 16 year cycles. A second-order polynomial trend, which peaked in the mid
1980s, was also evident in the data. Various factors including poor weather and a
related increase in trichstrongyle parasitic worms may have contributed to the decline
in grouse numbers since the mid 1980s (NYMNP, 2001). This decreasing trend in
grouse bags suggests that revenue generation and employment within the shooting
industry in the NYMNP declined from the mid 1980s until the late 1990s.
9.1.2.2 Breeding bird numbers as an indicator of ecological capital within the
NYMNP
The mean abundance of breeding birds recorded per 1km square surveyed
decreased significantly between 1994-2003. Although the overall population of
breeding birds in Britain has increased since 1970, the downward trend in the North
Yorkshire data mirrors the trend in both farmland and woodland bird species in
Britain (Defra, 2004). This suggests that the state of the wildlife and countryside
within the NYMNP is declining, and could suggest that biodiversity as a stock of
ecological capital within the farmland, moorland and forest ecosystems in the
NYMNP is declining. Inclusion of additional measures of biodiversity would confirm
this trend.
43
9.1.2.3 Water quality as an indicator of environmental capital within the NYMNP
The mean nitrate concentration in each of the four rivers monitored increased
between 1995-2002 (1990-2002 for the Esk). However, the increase was only
statistically significant for the Derwent. This will affect the biota within the river
ecosystem and reduce the health of the aquatic environment. It also suggests that
nitrogen run-off into river systems may have increased over the same period as this
would contribute to a reduction in available water cleansing capacity. Analysis of
fertiliser application data would be expected to corroborate these findings.
9.1.2.4 Population size, house prices and farm employment as indicators of human
and social and capital within the NYMNP
The population living in the NYMNP changed significantly between 1966-2001. The
overall trend fits a second order polynomial, and the decreasing tail on the curve
mirrors the steep rise in the average house price in the Park from 1997-2003.
Average house price in the NYMNP remained relatively stable between 1991-1997,
but then rose exponentially until 2003. This follows the general pattern observed
across Britain, but average house price within the NYMNP has risen more rapidly
than the national average since 1997, which probably indicates an increasing
demand for homes in this area. This suggests that communities within the NYMNP
are likely to be able to maintain a viable social structure. The number of people
employed in agriculture within the park has declined since the mid 1980s, in line with
the reduction in the total income generated from sheep farming (9.1.2.1). It is also
possible that the labour force employed in grouse shooting has declined since the
mid 1980s in line with the reduction in grouse numbers over the same period. Whilst
these declines may not reflect overall employment within the NYMNP, they may be
indicative of a decline in social structure, well-being and viability within the farming
and gamekeepering community.
9.1.3
Conclusion
The analyses presented here are based solely on the trend results for the individual
indicators of capital stocks and conclusions would be likely to change if the safety
margins to critical thresholds were considered as well as trends, and if different
weightings were applied to the health consequences of the combined trend and
safety margin results from individual indicators (as outlined in section 8.2.1).
However, several general features emerge. Profit generation from tourism is
increasing and this will impact on the farming and forestry sectors as well as
businesses in the service sector.
Forest and woodland businesses generate
revenue from visitor centres, accommodation rentals, bicycle hire and toll charges on
the scenic drive in Dalby Forest, and farm businesses also derive an increasing
proportion of their income from accommodation rentals, farm shops and amenity
provision. Set against this increase in tourism revenues, profit generation from
traditional farming sources and from grouse shooting is declining. These results
suggest that the composition of the labour force within the NYMNP is changing, and
whilst this may not be problematic for the NYMNP at large, the stock of human
capital available to the farming sector could be dropping towards a critical threshold.
The data analysed suggest that there is some cause for concern regarding the stock
of ecological and environmental capital within ecosystems in the NYMNP. The
decline in breeding bird numbers suggests that biodiversity is decreasing and that
watersheds within the NYMNP show some evidence of a declining capacity for
producing clean water. The roles played by agricultural run-off and/or increasing
visitor numbers on nitrate input into the river system is as yet uncertain.
44
Indicators that report on aspects of social capital in human communities in the
NYMNP paint a mixed picture. House prices in the NYMNP have increased faster
than the national average over recent years, suggesting that homes in the NYMNP
have become increasingly desirable. This probably implies that the town and village
communities within the NYMNP will retain a viable and cohesive social structure, but
the decrease in farm, and possibly gamekeeper, employment which was also noted
is likely to carry negative implications for the viability of the traditional farming
infrastructure and the stock of human capital available to those land-based
businesses.
On balance, the health of managed ecosystems within the NYMNP appears
increasingly reliant on tourism. Whilst it is likely that higher visitor numbers make a
beneficial contribution to stocks of financial, human and social capital within the
health assessment, there are also indications that higher visitor numbers may be
depleting stocks of environmental and ecological capital.
9.2
Case Study 2: The Ythan catchment in Aberdeenshire
The catchment of the River Ythan covers c. 640 km2 rising to a few 100 metres in
altitude to the north of the city of Aberdeen in the north-east of Scotland. Several
major tributaries join the Ythan towards its lower reaches, some almost as large as
the main river itself, which is never more than a few 10s of metres wide, even at the
estuary. The estuarine ecosystem carries populations of nationally important flagship
bird species including eider duck (Somateria mollissima), redshank (Tringa totanus),
and shelduck (Tadorna tadorna), and has been studied intensively for more than 40
years by staff and students at the University of Aberdeen’s Culterty Field Station.
Here, we describe trends in indicators of environmental, ecological, financial, human
and social capital stocks for the catchment as a whole.
Land use in the Ythan catchment is predominantly agricultural, and significant
changes in agricultural practice have occurred within the area during the past 40
years, mirroring those elsewhere in Scotland (Raffaelli et al.,1989, 1999, 2004).
Principal changes have been

the preferential growing of subsidised cereals, such as wheat and barley, at
the expense of the more traditional oats

the introduction of novel crops, such as oil-seed rape

an increase in the total land area under fertiliser-hungry cereals and rape, at
the expense of grassland, especially rough grazing

a shift towards winter and autumn sown cereals, such that land is tilled at a
time of high precipitation and run-off

an increase in pig production
These land-use changes are unambiguously reflected in the water quality of the
River Ythan. Since 1958, there has been a 2-3 fold increase in the concentration of
total oxidised nitrogen (almost entirely nitrate) in river water (Raffaelli et al. 1989;
1999; 2004) and a similar pattern is seen within the estuary. The Ythan catchment
was consequently declared a Nitrate Vulnerable Zone (NVZ) under the European
Union’s Nitrates Directive and the UK is now required to take steps to reduce nitrate
loadings within the river system to ameliorate the impact on the ecology of the
estuary.
45
Here we provide a further illustration of the application of the MEHTA approach to the
assessment of ecosystem health. This implementation also investigates whether
relevant data become more or less readily available as spatial scale decreases (the
Ythan catchment covers only 44% of the land area of the NYMNP) and temporal
coverage lengthens (data are required from the 1960s onwards to track the
ecosystem health implications of land use changes).
The aims of this analysis are identical to those described for the NYMNP (11.1), with
the caveats as detailed in section 10.3. A health assessment was undertaken using
the protocol already described for the NYMNP and the same capital stocks were
considered. The individual indicators selected for the various capital stocks are
described in the following section.
9.2.1
Indicators of natural and man-made capital stocks
9.2.1.1 Indicators of financial capital in the Ythan catchment
Agriculture is an important source of revenue within the Ythan catchment and
indicators reflecting the economic status of the farming industry were chosen to
provide an indication of the stock of financial capital available to land management
businesses within the catchment. A direct measure of farm income within the
catchment was unavailable, however, although crop areas and stock numbers from
all the farms within the catchment over the period 1968-1991 and the associated
farm gate prices were known (Raffaelli et al. 1989; 1999; 2004). Ideally, yield levels
and subsidy payments over the same period should also be considered to produce a
full measure of farm income, but these data could not be obtained. It is likely that
tourism has also generated substantial revenue within the Ythan catchment,
particularly over recent years, but no data detailing tourist numbers or tourism
revenues have as yet become available. Given these shortcomings, the financial
capital stock was removed from the health analysis until such time as the remaining
relevant data become available.
9.2.1.2 Indicators of ecological capital in the Ythan catchment
The abundance of wader birds recorded on the Ythan estuary (Raffaelli et al., 1999),
and an index of the abundance of breeding birds (BTO, 2004) were both used as
indicators of biodiversity as a stock of ecological capital within the catchment. These
indicators report the status of different bird species and so are regarded here as
surrogates for different aspects of biodiversity within the catchment. The breeding
bird survey covered the whole of the Grampian region as data were not available at
finer spatial resolution.
9.2.1.3 Indicators of environmental capital in the Ythan catchment
An index of water quality in the Ythan (Scottish Environmental Protection Authority
(SEPA) data, unpublished) was used as a measure of aquatic ecosystem health, and
as a surrogate for available water purification capability as a stock of environmental
capital, similar to that used in the NYMNP health assessment.
9.2.1.4 Indicators of human and social capital in the Ythan catchment
The population living within the catchment (Aberdeenshire Council, pers. comm.),
and the number of those that were employed (Office of National Statistics, pers.
comm.) were used as indicators of the stocks of human and social captial within the
catchment.
46
9.2.2
Analysis and results
Trends within the data, together with their associated confidence intervals (95%),
were analysed using simple and multiple linear regression in a similar manner to that
already described for the NYMNP. Safety margins surrounding critical thresholds and
weightings to apply to individual results could be obtained following stakeholder
consultation, portfolio analysis and expert assessment as described previously
(section 8.2), but the available timescale precluded the use of these procedures in
this preliminary study. The following subsections report the results of regressionbased trend analysis (Table 9.2). An example of an indicator with confidence
intervals (95%) around individual predictions is provided in Fig. 9.2.
Table 9.2 Summary of trends in indicators of natural and man-made capital stocks in
the Ythan catchment
1
Financial
a full set of relevant data could not be obtained, so assessment of trends in
the stock of financial capital within the catchment is still awaited
Ecological
Breeding
birds
1994-2003
(exc.2001)
F2,6=5.203
Wading
birds
1969,’70,’73’78,’80-‘82,’88‘95
F2,16=5.62
Environmental Water
quality
1980-1990
(exc.1985)
F1,8=17.850
Social &
Human
1901-2001
(every decade
exc.1941)
F2,7=10.029
1984,’87’89’91;
1993,’95-‘98
F3,5=8.665
Employment
1
Stein’s adjusted R2
Model Fit
Adj. R2
Indicator
Population
Time span
R2
Capital Stock
0.634 0.482
P=0.049
0.413 0.212
P=0.014
0.691 0.563
P=0.003
0.741 0.512
P=0.009
P=0.02
0.839 0.499
Trend
47
4
Breeding Bird Survey Index
3.5
3
2.5
2
1.5
BBSindex
low er individua 95% CIl
1
upper individual 95% CI
predicted trend
0.5
0
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
Year
Fig. 9.2 Actual and predicted breeding bird survey index from the Grampian region
showing 95% upper and lower confidence intervals for individual predictions.
9.2.2.1 Revenues from land-based industries as indicators of financial capital in the
Ythan catchment
A full set of relevant data could not be obtained to allow trends in the stock of
financial capital available to land management businesses within the Ythan
catchment to be assessed over the period of interest. It is hoped that the missing
elements of data will be obtained at some point in the future.
9.2.2.2 Bird numbers as indicators of ecological capital in the Ythan catchment
The mean abundance of wading birds recorded on the Ythan estuary followed a 2nd
order polynomial curve, peaking in the early 1980’s. The decline recorded after this
point may be a response to the negative impact which changing patterns of
agriculture within the catchment exerted on water quality (Raffaelli et al. 1999, 2004).
Data detailing the abundance of breeding birds also followed a second order
polynomial curve with a minimum occurring in 1998. These data were only available
from 1994-2003, and it was therefore not possible to compare breeding bird
abundance pre- and post- eutrophication. The current increase in abundance may
be as result of recent conservation initiatives, for example NVZ and ESA
(Environmentally Sensitive Areas) measures.
9.2.2.3
Water quality as an indicator of environmental capital within the Ythan
catchment
Water quality in the Ythan estuary decreased significantly between 1980 and 1990,
reflecting increased fertiliser use and slurry application within the catchment. This
matches the explanation given in the preceding section regarding the decrease in
wading bird numbers recorded on the estuary over this time period.
48
9.2.2.4 Population size and employment as indicators of human and social capital
within the Ythan catchment
The population within the catchment followed a second order polynomial curve with a
minimum around the early 1950s. The population has increased and major changes
in land use have occurred within the catchment since the late 1960s. Many factors
may have contributed to this increase, including the boom in the oil industry across
Aberdeenshire as a whole. Employment figures within the catchment showed no
significant change between 1984 and 1991, but, post-1991, the number of people in
employment increased significantly.
9.2.3
Conclusion
The trend results revealed that indicators of ecological and environmental capital
within the catchment declined significantly with the onset of eutrophication (up to
1990). The decline in wading birds and water quality reflected a reduction in the
health of the aquatic ecosystems within the catchment probably as a consequence of
increased nitrate run-off into the river system caused by fertiliser and slurry
application. The increase in population, however, suggests that the area became
more desirable towards the end of the 1980s, but this may be a result of the
employment generated within the oil industry during this period. The overall
conclusions for the health of managed ecosystems within the Ythan catchment are
therefore ambiguous as stocks of social and human capital appear to have increased
whilst ecological and environmental capital stocks have declined, and it has not been
possible to establish the position with regard to the stock of financial capital available
to land management businesses.
10 Data resolution and availability
Preliminary analyses of the trends in different capital stock indicators for two
contrasting systems, an upland national park and a lowland river catchment, revealed
issues for both of data availability as well as of data resolution. Some of these could
be resolved with further time had been available, but others are real knowledge gaps.
These are discussed below.
10.1 Spatial resolution
The ecosystem is widely regarded as the most appropriate scale at which to address
questions of ecosystem health, but the most relevant data are generally not collected
at this scale. Ecological and environmental data are often collected using a map
grid-based system, such as the Ordnance Survey National Grid. Records of species
distribution and abundance are based on 10-km (or, less commonly, 1-km) squares
within this grid. Other environmental data, such as water quality characteristics, are
available for specific stretches of rivers (i.e., not on a grid). Social and economic
data tend to be collated at the scale (and shape) of administrative units. For
agricultural census data, these may be parishes or parish groups, and for social data,
the boundaries may be much wider, for example, administrative authorities or
regional boundaries. Even within one sector, there may be variations. Thus, in
England, data on agricultural cropping areas (from the June Census) are available at
the level of the parish group, but data on farm incomes (from the Farm Business
Survey), based on a sample of farms only, are only available nationally or regionally
by farm type.
49
The major challenge confronting any attempt to assess the status of different
categories of ecosystem capital within an assessment of ecosystem health is
therefore the collation of these data at an appropriate and common spatial scale. We
have attempted to do this as far as possible for our case studies of the North York
Moors National Park (NYMNP) and the Ythan catchment. However, these case
studies also illustrate the problems involved. For example, for the NYMNP case
study, river quality measurements were confined to rivers arising within the National
Park itself and grouse shooting revenues were based on a sample of estates within
the National Park, whereas the breeding bird data were aggregated over the whole of
North Yorkshire (Fig. 10.1a). There were similar differences in spatial resolution of
the data relating to the Ythan estuary (Fig. 10.1b).
Some previous research has been conducted to convert administrative area-based
data to surfaces with respect to human population densities (Bracken & Martin, 1989;
Martin, 1989) and agricultural census and environmental data (Moxey, White &
O'Callaghan, 1995; Lord & Anthony, 2000; Edinburgh University Data Library, 2004).
This would allow more direct comparison of data from different sources, and also
enable data to be aggregated or disaggregated to suit the objectives of the analysis.
However, the methodology is not straightforward and issues of confidentiality arise
with respect to the potential disclosure of confidential information (Office of National
Statistics, 2002) Work on new methods for enhancing the compatibility of
environmental and social datasets is the subject of a forthcoming project that the
University of York is co-ordinating within the Rural Environment and Land Use
(RELU) programme.
NYMNP
INDICATOR
1km x
1km
Parish
County
Region
Farm type
Farm income
Farm employment
People visiting
Use of visitor centre
Visitor days
Breeding birds
Common birds
Grouse
Water quality
Population
House prices
Employment
Fig. 10.1a Spatial resolution of different indicator datasets for the North York Moors
National Park
50
YTHAN
INDICATOR
1km x
1km
Parish
County
Region
Crop area
Stock no.
Water quality
Algae cover
Shore birds
Population
Employment
House prices
Fig. 10.1b Spatial resolution of different indicator datasets for the Ythan catchment.
10.2 Temporal resolution
As well as being recorded at different spatial resolutions, data across the natural and
social sciences may also be recorded at different temporal resolutions. For example,
the Agricultural Census and the Farm Business Survey are conducted annually,
whereas the human population census takes place every ten years. Data on some
taxonomic groups, e.g. breeding birds, are collected annually whereas data on
others, such as some mammals, may be collected only once every 10 years (see
JNCC (2003) for an inventory of UK biological monitoring programmes). Since time
series analysis forms a key part of any ecosystem health assessment that can be
used to provide a means for prediction or scenario testing, rather than just a
snapshot in time, temporal mis-matches in data are also problematic. For example,
in our NYMNP case study, data on grouse bags were available annually from 1970
up to 1999, but other annual datasets did not start until much later, e.g. visitor days
available from 1992 (Fig. 10.2a). For the Ythan, data on crop area and stock
numbers were available annually from 1970 until 1991, but some data, such as mean
abundance of wading birds, were collected at irregular intervals (Fig. 11.2b). For
both case studies, some datasets were only available at much longer time intervals.
51
NYMNP
INDICATOR
1970
1980
1990
2000
Farm type
Farm income
Farm employment
People visiting
Use of visitor centre
Visitor days
Breeding birds
Common birds
Grouse
Water quality
Population
House prices
Employment
Fig. 10.2a Temporal resolution of different indicator datasets for the North York
Moors National Park
YTHAN
INDICATOR
1970
1980
1990
2000
Crop area
Stock no.
Water quality
Algae cover
Shore birds
Population
Employment
House prices
Fig. 10.2b
catchment.
Temporal resolution of different indicator datasets for the Ythan
52
10.3 Data availability
One of the main problems in applying any evaluation of ecosystem health in the UK
is the dispersed nature of the data resources. For both the NYMNP and Ythan case
studies, the data were obtained from a number of different sources, and some
datasets required a significant amount of modification before they could be used in
the analysis. Some agricultural data, especially those relating to income, are accessrestricted to maintain confidentiality, so this may affect the scale at which certain data
relating to financial and human capital are available. Some biological data for rare
species may also be unavailable at high resolution, and other biological datasets are
in the ownership of specific individuals and not available for general use.
For specific areas that are designated by legislation, such as National Parks, relevant
data are often more readily accessible, since commonly there has already been
some work done in collating data from various sources. However, for areas that are
defined by geography rather than administrative boundaries and where large areas of
the land may be in private ownership, such as the Ythan estuary, there are much
greater problems in obtaining data.
The general availability and transferability of data has been recognised as an
important issue by various organisations and is a keystone of some current research
programmes, such as Defra’s Multi-Agency Geographic Information for the
Countryside (MAGIC) initiative and the Research Councils’ RELU programme.
11 Conclusions and recommendations
11.1 Is the ecosystem health concept valuable?
Understanding and assessing ecosystem health is important because ecosystem
health underpins sustainable development. However, because of the widespread
and increasing impacts of humans throughout the world, ecosystem health is of
limited use as a concept when it is applied to the non-human biological components
of a system in isolation. For this reason, ecosystem health in the present report
encompasses the environmental, economic and social dimensions as well, to provide
a more holistic assessment of sustainability. It also provides a means through which
society’s views on ecosystems and the environment can be formally incorporated into
this assessment via participatory approaches. This is not to say that the views of
society should entirely supplant expert judgements, but they should be included as
far as possible in any overall evaluation of sustainability and conservation issues.
The measurement of ecosystem health also has the potential to highlight
heterogeneities in the way that stakeholders in different areas value stocks of the
different forms of natural and man-made capital within managed ecosystems. Such
differences may produce geographical differences in the outcome of an ecosystem
health assessment.
11.2 Operational approaches to assessing ecosystem health
Many of the earlier proposals for ecosystem health indices, as discussed in the
previous chapters, are primarily useful as conceptual rather than operational models
53
(e.g. Costanza, 1992). Other models are dependent on detailed measurements from
food webs that can only come from intensive, long-term empirical studies (e.g.
Hannon, 1985; Ulanowicz, 1992; Jørgensen, 1995). More recent work highlighting
the importance of humans in an assessment of ecosystem health (Xu & Mage, 2001)
has extended the assessment criteria in conceptual terms, but the only operational
tool to be used to date is the HEHI (Aguilar, 1999).
The MEHTA approach differs from the HEHI approach in that HEHI is an assessment
based on the values of specific indicators at one point in time, whereas MEHTA is
explicitly based on an assessment of trends in indicators of stocks of natural and
man-made capital over time. It therefore provides a means by which changes in
ecosystem health can be monitored, and allows the rate and direction of this change
to be evaluated with respect to specific critical thresholds. Both approaches allow
participatory involvement along with expert knowledge to determine the weightings
attached to various indicators, which is an important criteria in any health
assessment. One feature of all these approaches to ecosystem health assessment is
that they do not necessarily imply cause-effect relationships. Causality can only be
inferred by investigating trends in several indicators over time or via comparisons
between ecosystem health assessments of different areas.
11.3 What is the appropriate spatial extent for ecosystem health assessment?
Assessments of ecosystem health should be conducted at an environmentally
meaningful scale or spatial extent, but defining appropriate system boundaries is not
straightforward. There is often a mismatch of ecological, political and geographical
boundaries. A river basin represents a fairly well-defined area that is environmentally
self-contained. However, it may not be ecologically self-contained, since animals
and plants may migrate in from elsewhere, and in a country such as the UK, it is
extremely unlikely to be self-contained in terms of the human population or flows of
natural or man-made products (e.g. food, machinery, electricity etc.). Indeed in the
global economy, impacts may be felt far away from their sources, and truly isolated
areas are very rare. Nevertheless, a catchment-based approach is probably the
minimum meaningful spatial extent for this type of analysis.
11.4 Data availability and accessibility
The two case studies in this report have highlighted the inconsistencies in data
availability in time and space. The MEHTA approach is very dependent on
consistent time series data, so data gaps may affect, and potentially bias, an overall
ecosystem health assessment.
The resolution at which data are available is a critical issue, since the use of coarseresolution data may obscure critical trends in specific indicators that are occurring at
a fine resolution, e.g. within specific catchments. Although socio-economic data are
collected at a fine resolution, they are often aggregated to a coarse resolution prior to
release due to potential disclosure issues. Largely because of these disclosure
issues, data become less readily available at finer resolutions and smaller spatial
extents. This is particularly true for agricultural data where profitability rather than
areas of specific crops or land uses is concerned, or where the application of
pesticides is of relevance (e.g. the results of Defra’s Pesticides Usage Survey are
reported for the UK as a whole, although they are based on surveys of individual
holdings). This can cause problems for assessments of ecosystem health where the
areas in question are relatively small, e.g. the Ythan catchment.
54
The accessibility of data is also very different between different host organisations.
For example, Environment Agency data are readily available online, whereas SEPA
(Scottish Environment Protection Agency) data are not.
There are certain
programmes that are seeking to redress these data availability problems, such as
Defra’s MAGIC programme. This type of initiative shows promise, but since there is
currently such a wide variety of relevant data, a single, central data depository is not
a realistic option. It would also mean that the data became further removed from
their source, potentially leading to problems with interpretation. Instead, efforts
should focus on ensuring that organisations holding relevant data do so in a
consistent manner, possibly through introducing a code of best practice for data
handling and availability, so that the mechanisms for accessing data are as
consistent as possible between different organisations.
At this stage it is difficult to state precisely how specific data elicitation schemes
should be extended. This is partly because the measures best suited to particular
case studies and the accessibility of data relating to them may vary in different
countries and regions. However, the schemes should continue to be reviewed and
existing data items should only be dropped or modified after very careful
consideration, especially given the importance of time series data in assessing
ecosystem health.
11.5 Gaps in knowledge
Ecosystem health is a relatively young subject, and paradigm shifts in terms of
understanding may occur. The interdisciplinary nature of the subject has led to very
different approaches being taken, and there is no generally accepted methodology
for evaluating ecosystem health, or even a universally accepted definition of its
scope. Nevertheless, one key area where there are major gaps in understanding is
the relationship between social and human capital, and the other three stocks of
capital (financial, ecological and environmental) within ecosystems and in particular
the role of social and human capital in promoting conservation, and/or use of these
other stocks.
Mechanisms by which the relative values which society ascribes to stocks of
financial, environmental and ecological capital, and the way in which these relative
values can be incorporated into the formal ecosystem health assessment, also
require further work. Understanding the weightings of the different components in
relation to social capital and governance is a critical issue for policy-makers in terms
of conserving ecosystem health and enhancing sustainability.
11.6 Summary of key findings and recommendations
Many of the indicator monitoring systems presently used in the UK and Europe focus
on specific ecological, environmental, or social elements which are sometimes
difficult to relate to the overall health of the broader system in which all these different
elements are embedded and interact. In addition, the relationship between many of
these measures to capital stocks and ecosystem services is not always articulated or
clear. If environmental quality and social well-being are to be maximised, then the
ecosystem health approach, which is both holistic and has measures unambiguously
related to capital stocks and services, offers considerable potential.
Implementing this approach operationally will require a shift in thinking at political and
scientific levels. Humans need to be seen as part of, not apart from, the ecosystem,
since human behaviour is now the main driver of environmental change. Maintaining
human well-being will require monitoring of natural and social capital stocks and the
55
interactions between them. In this respect, holistic indicator systems, such as HEHI
and MEHTA described here, offer considerable potential for evaluating the health of
coupled social-ecological systems. Present policies focused on individual
environmental or social elements will be unlikely to protect underlying stocks, the
services which flow from them and human well-being.
There are also clear technical challenges which need to be met if the potential of the
ecosystem health approach is to be realised. The most relevant natural and social
data have often not been collected, or have been collected at different scales, or are
not easily accessed due to issues of confidentiality. These issues are not
insurmountable: techniques can be developed for addressing issues of the mismatch of scale, data disclosure policies revised and novel and more relevant forms of
data requested by policy makers and agencies. Perhaps more challenging politically
will be the derivation of acceptable levels to society of stocks of natural capital. If
policies are to be successfully implemented with respect to the regulation of natural
capital, so that the desired ecosystem services can be maintained, then there must
be both scientific and social inputs into those discussions which set those thresholds.
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