Regional Index of Ecological Integrity: A need for sustainable management of natural resources

Ecological Indicators 11 (2011) 220–229

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Review

Regional Index of Ecological Integrity: A need for sustainable management of natural resources Mohammad Imam Hasan Reza a,b,∗ , Saiful Arif Abdullah a,1 a b

Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor Darul Ehsan, Malaysia Department of Botany, University of Chittagong, Chittagong 4331, Bangladesh

a r t i c l e

i n f o

Article history: Received 15 December 2009 Received in revised form 22 July 2010 Accepted 22 August 2010 Keywords: Anthropogenic disturbances Conservation planning Policy/management Representativeness Regional scale Spatial processes

a b s t r a c t An ecosystem is a complex composition of physical, chemical and biological components. This complex system remains in a healthy state if the system can maintain the ecological equilibrium among its components. Anthropogenic disturbances are the prime stressors that affect this equilibrium through creating fragmentation, ecosystem sensitivity, loosening landscape connectivity and disrupting ecological integrity. As different types of ecosystem are interconnected, a comprehensive monitoring and evaluating criteria is needed for measuring its integrity at regional level for conservation planning. A Regional Index of Ecological Integrity can be a suitable approach for sustainable management of regional ecosystem. Therefore, this paper presents (i) the characteristics of ecological integrity, (ii) the spatial processes induced by anthropogenic stressors and (iii) an approach to develop a composite Regional Index of Ecological Integrity (RIEI). The prime objective is to establish a thought and a way to develop a composite index of ecological integrity at the regional level. Here, we demonstrate different compositional, structural and functional indicators/indices related to fragmentation, representativeness of protected area, ecosystem sensitivity, and landscape connectivity for the development of a Regional Index of Ecological Integrity (RIEI). © 2010 Elsevier Ltd. All rights reserved.

Contents 1. 2. 3. 4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The characteristics of ecological integrity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changes in spatial processes due to anthropogenic pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The approaches for combating the challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Regional Index of Ecological Integrity (RIEI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. The work plan for developing an effective RIEI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Fragmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Representativeness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Ecosystem sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4. Landscape connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Calculation and final index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

220 221 222 222 223 224 225 225 226 226 226 227 228 228

1. Introduction ∗ Corresponding author at: Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor Darul Ehsan, Malaysia. Tel.: +60 3 8921 4161; fax: +60 3 8925 5104. E-mail addresses: reza [email protected], [email protected] (M.I.H. Reza), saiful [email protected] (S.A. Abdullah). 1 Tel.: +60 3 8921 4151; fax: +60 3 8925 5104. 1470-160X/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2010.08.010

In many parts of the world, ecological integrity has become a popular approach for conservation planning (Westra et al., 2000; Manuel-Navarrete et al., 2004; Borja et al., 2009). Ecological integrity can be defined as the capacity to support and maintain the balanced and integrated ecosystem in a particular region (Karr and

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Dudley, 1981; Karr, 1996; Parrish et al., 2003). It is also considered as a capacity and an indication of the degree of self-organization (Müller et al., 2000). Originating from the ethical concept put forwarded by Leopold (1949), ecological integrity has been measured to assess ecological condition of aquatic and terrestrial ecosystems (Andreasen et al., 2001; Zampella et al., 2006; Hargiss et al., 2008). Ecological integrity has emerged as a fundamental basis for the implementation of natural resource protection, for example, the Clean Water Act (CWA) in 1972 in the United States (Barbour et al., 2000) and the Austrian Water Act in 1990 (Moog and Chovanec, 2000). The general problem of all ecological analyses and sustainable management decision processes is the complexity of the ecosystems (Müller et al., 2000), particularly in the tropics where their natural forests are losing rapidly (Laurance, 1999; Laurance et al., 2004; Curran et al., 2004). Moreover, due to restricted access to modern information and communication technologies, local decision makers rely on the expertise of local academics, forest managers, cattle breeders and/or farmers (Kampichler et al., 2010). In this context, an approach for the development of an index of ecological integrity has been undertaken to simplify the sustainable management paradigm. Many approaches have been proposed for the development of indices of ecological integrity (e.g. Karr and Chu, 1999; Angermeier and Davideanu, 2004; Ortega et al., 2004; Solimini et al., 2008; Rothrock et al., 2008; Borja et al., 2009), but they are applied to very specific areas of aquatic or terrestrial ecosystems. Till date there is no ecological integrity index to represent the entire region comprising both aquatic and terrestrial ecosystems (Slocombe, 1992; Andreasen et al., 2001; Borja et al., 2009). Moreover, biodiversity conservation in multi-functional, human-dominated landscapes needs a coherent, large-scale spatial structure of ecosystems (Opdam et al., 2006) where different types of ecosystems are nested side by side in a regional context (Bailey, 1996). Therefore, it may be justified to measure ecological integrity at regional scale to get a complete picture of ecosystem composition, structure and function. While many researchers have suggested the need for a suitable index at regional level (Andreasen et al., 2001; Borja et al., 2009), thus far no effort has been undertaken to develop such an approach. Nevertheless, understanding the characteristics of ecological integrity and spatial processes associated with anthropogenic stressors is crucial and prerequisite to developing an index of ecological integrity at the regional level. Therefore, this paper presents (i) the characteristics of ecological integrity, (ii) the spatial processes induced by anthropogenic pressures and (iii) an approach to develop a composite Regional Index of Ecological Integrity (RIEI). The prime objective is to establish a thought and a way to develop a composite index of ecological integrity at the regional level. To achieve the objective, this paper begins with a discussion on the characteristics of ecological integrity in regional context. Here, we tried to identify the components of ecological integrity, how its capacity is related to compositional, structural and functional attributes of an ecological system and also, their relationship with self-organization of an ecosystem. In the following section, we discuss specifically the spatial processes induced by anthropogenic stressors. In this part, we consider the spatial processes that are suitable for assessment of ecological integrity at regional level. This is followed by a work plan to develop an approach for Regional Index of Ecological Integrity (RIEI), and finally some conclusions are drawn.

2. The characteristics of ecological integrity Leopold (1949) was a pioneer in defining ecological integrity as “A thing is right when it tends to preserve the integrity, stability

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Fig. 1. Self-organization components of a typical ecosystem for the evaluation of ecological integrity. Exergy trapped by the system which used to generate structure and modified energy drives the different functional processes. Anthropogenic activities putting pressure on the window of viability. It may decrease energy capture, storage capacity, cycling, efficiency, heterogeneity and accelerate nutrient loss and will decrease ecological integrity. If spatial processes like fragmentation, ecosystem sensitivity, landscape connectivity of this ecosystem can be measured thus ecological integrity can also be measured. Modified after Müller et al. (2000).

and beauty of the biotic community. It is wrong when it tends otherwise”. He did not give a clear definition of the term ‘integrity’ in his article on land ethics, but he initiated a thought on sustainability. Later the concept was clarified by several works, specifically by Karr and his co-workers (Karr, 1981; Karr and Dudley, 1981; Karr and Chu, 1999), where they defined ecological integrity as “the ability of an ecosystem to support and maintain a balanced, adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of a natural habitat of a region”. By this definition, the features of ecological integrity include ecosystem health, resilience and self-organizing capacity (see Joergensen, 1992; Müller, 1998, 2005). This is the feature of a natural ecosystem. An ecosystem is an open system where exergy (total available energy radiated by the solar system to the earth) is entrapped by the system and transferred within metabolic reactions (e.g. respiration, heat export) (see Fig. 1). Self-organized ecological systems try to build up ordered structures and store the imported exergy within biomass, detritus and information (e.g. genetic information), which can be indicated by structural diversities (Joergensen, 2000). In a disturbance-free environment, the system will advance to a more complicated state of heterogeneity, increasing species richness and rising in connectedness, and many other ecological attributes will follow a similar trajectory (sensu maturity of ecosystem, see Odum, 1969). This complexity can be characterized as the complex chemical, biological, and social interactions in an ecological system (Colwell, 1998), which develop through the multiplicity of interconnected relationships and levels (Ascher, 2001) (see also Fig. 1). So, there is a significant relationship among compositional, structural and functional attributes of an ecosystem. During these synergetic processes, creation of macro-structures from microscopic disorder leads to the formation of emergent properties and gradient (Müller, 2005). Thus, the features of ecological integrity also include ecosystem functions, ecosystem thermodynamics, gradient degradation and ecological orientors (Müller and Leupelt, 1998; Joergensen, 2000). Therefore, careful selection of indictors from compositional, structural and

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Fig. 2. Structural characteristics of ecological integrity at regional level. Different spatial processes show their impacts on ecological integrity.

functional attributes of ecosystem can successfully represent the ecological state and integrity of a region. 3. Changes in spatial processes due to anthropogenic pressures Several spatial processes occur because of anthropogenic activities like agriculture, logging, aquaculture and urbanization (Abdullah and Nakagoshi, 2006, 2007; Riley, 2007) (Fig. 2). Such processes are synchronized and proliferated according to the spatial and temporal nature and volume of the pressures (Nitschke, 2008). The initial spatial process to affect naturalness is fragmentation of habitat. Habitat fragmentation affects compositional as well as structural and functional aspects of ecosystem (Harris, 1984; Franklin and Forman, 1987; Arroyo-Rodriguez et al., 2007). Its effects lead to changes in species composition, community structure, population dynamics, behavior, breeding success and a range of ecological and ecosystem processes (Laurance et al., 2002; Fahrig, 2003; Opdam and Wascher, 2004; Henry et al., 2007), for example, pollination, decomposition, nutrient cycling, seed dispersal and predation (Harrison and Bruna, 1999). Habitat fragmentation destroys links or connectivity between patches of habitat (Fahrig and Merriam, 1994; Foppen et al., 1999); both of its structural connectivity (e.g. increasing number of patches, reduction in patch size, distance between patches) as well as functional connectivity (dispersal ability of organisms as response to the discrete patches) (Taylor et al., 1993; With et al., 1997; Tischendorf and Fahrig, 2000). Loss of connectivity may cause loss of genetic diversity (Gibbs, 2001), create barrier to dispersal (Cramer et al., 2007; Haddad and Baum, 1999), species extinction (Foppen et al., 1999; Donovan and Flather, 2002) and disruption of biotic interactions (Kruess and Tscharntke, 2000). Fragmentation and loss of connectivity reduce the quality of the habitat, which eventually increases sensitivity to disturbances (Nell, 2008). The particular habitat becomes an environmentally sensitive area which loses its ecological capability to support flora and fauna. The combination of fragmentation, loss of connectivity and increasing sensitivity represents the vulnerability of the ecosystem

(see Penghua et al., 2007). Vulnerability is the state of susceptibility to harm from exposure to stresses associated with environmental and social change and from the absence of capacity to adopt (IPCC, 2001; Tyler et al., 2007). Severe anthropogenic disturbances especially intensive land use change makes the species and also their habitat vulnerable (e.g. White et al., 1999; Jackson et al., 2004; Metzger et al., 2006; Mercer et al., 2007). To protect the natural landscape from further degradation and to conserve biodiversity, a protected area system has been established (Timko and Innes, 2009). Policy and management are responsible to determine habitat representativeness in the protected area system. However, in most cases, the habitats in a particular area or region are less represented by the protected area system (Armenteras et al., 2003; DeFries et al., 2005; Timko and Innes, 2009). All these create degradation and decrease the capacity of self-organization of the ecological system and hamper the integrity of the system (Fig. 2). 4. The approaches for combating the challenges The components of ecological integrity include ecosystem health, biodiversity, sustainability, stability, naturalness, wilderness and beauty (Barbour et al., 2000; Andreasen et al., 2001) (see Fig. 3). Ecosystems with high integrity should be relatively resistant to environmental changes and stresses (Andreasen et al., 2001). Despite some debates and criticism over the last two decades (Soule and Lease, 1995; O’Neill, 2000), the concept of ecological integrity is considered reliable for conservation of natural ecosystems (Barbour et al., 2000; Andreasen et al., 2001; Ortega et al., 2004). It is impossible to measure all the potential components in an ecosystem. For example, no two species exist in the same niche and no single species should be expected to represent the condition of an entire ecosystem (Cairns and Van der Shalie, 1980). Ecological integrity is generally tested with several indictors that represent components of the structural, compositional and functional attributes of an ecosystem (Karr, 1981; Noss, 1999; Carignan and Villard, 2002; Müller, 2005). However, in most cases the assessment is difficult to understand by the public and stakeholders. In

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Fig. 3. Typical component feature of ecological integrity (biodiversity and beauty represents stability, sustainability, naturalness, and wilderness).

this circumstance, a composite index can provide a general feature of ecological integrity of an ecosystem (Andreasen et al., 2001). In developing the composite index, many indicators are selected to represent various components of the ecosystem (e.g. Karr and Chu, 1999; Andreasen et al., 2001; Carignan and Villard, 2002). Nevertheless, prior to this process, it is a prerequisite to review and construct the characteristics of the index whether it represents all the attributes of the ecosystem comprehensively.

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4.1. Regional Index of Ecological Integrity (RIEI) A Regional Index of Ecological Integrity must represent the vital attributes of the regional ecosystems. Though a set of indicators may vary according to the region, the selection must be based on the general characteristics of the regional ecological integrity. This selection process is a vital part in the development of such index where stakeholders, decision makers and land mangers closely need to cooperate with scientists. Andreasen et al. (2001) outlined six characteristics of an effective index of ecological integrity for terrestrial ecosystems, which include multi-scaled, grounded in natural history, relevant and helpful, flexible, measurable and comprehensive. Earlier, Riley (2000) also suggested some properties for index of ecological integrity. These are universality, probability, sensitivity to changes, simple, inexpensive, analyzable with existing historical dates, and have wide use. Considering all these suggestions with those of Dale and Beyeler (2001) and the Convention on Biological Diversity (1999), we suggest a set of characteristics for an RIEI (Table 1). The characteristics of the RIEI are described below: • Multi-scaled: A dynamic and complex ecological system is the organization of flows, storages and regulations of energy, matter, substances and information. To understand their general features and validate the implicit models, ecological components from compositional, structural and functional attributes have to be taken into account (Müller, 2005). In a regional context, although the suite of indices may vary from site to site and also region to region, they are suitable tools to represent the state of complex organization of ecosystems (Müller et al., 2000). Thus, a Regional Index of Ecological Integrity must consider multi-scales to measure the ecological states of a region (see O’Neill et al., 1989; Andreasen et al., 2001). • Grounded in history and succession: Both organisms and their habitat, successional state, environmental consequences must fit with the selection of indicators (Carignan and Villard, 2002). Furthermore, Grumbine (1994) highlighted on maintenance evolutionary and ecological processes. Considering all the suggestions and studies, selection of representing indicators must relate histories of organisms, evolution, population dynamics, importance on existence (e.g. endangered, endemism, etc.), and

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fitness to the environmental changes. Therefore, the following categories need to be considered in the indicator selection for developing a Regional Index of Ecological Integrity. 1. Natural history of organisms, 2. history of successional attributes and evolution, 3. conservation importance (e.g. endangered, endemic, threatened), and 4. adaptations to environmental changes. Relevant and helpful: For the effectiveness of such effort, the index must address endpoints and values that society relates to, such as spiritual, cultural, religious, and esthetic values (see Jüdes, 1998). Ecosystem goods and services (water quality, air quality, flood mitigation, waste treatment), and recreational values and commercial values (potentialities of fisheries, timber, tourism and related business) (Andreasen et al., 2001) are examples of benefits that can accrue from the effort. People (e.g. stakeholders and decision makers) must understand and realize that the approach is helpful for them with both short-term and long-lasting facilities and services (Bossel, 1998). Simple and flexible: An index of ecological integrity will be effective if it is simple and understandable to the public, decision makers and stakeholders of different hierarchies as they will be involved in implementation (Andreasen et al., 2001). There must be some awareness and capacity building programmes to be arranged to make the people competent to handle such Regional Index of Ecological Integrity. So, it must be simple and understandable for all the personnel involved (Convention on Biological Diversity, 1999). Particularly this effort is needed for a regional scale as the approach required a long-lasting period and a public involvement is important. Moreover, the index must be flexible to accommodate new relevant information to make the approach fit with the changes (Dale and Beyeler, 2001). Adjustable: A regional landscape is wide and every ecosystem within it is interconnected by ecological attributes, e.g. compositional, structural, and functional (Müller, 2005). Furthermore, the structural, compositional and functional components of the region have a long interactive chain with the other region of its surrounding. That is why a Regional Index of Ecological Integrity should be adjustable with its surrounding ecoregions by ecological and landscape attributes (Riley, 2000). Uses of airborne, satellite data for measuring different components of the index have the ability to adjust ecological attributes in a wider range. Remote sensing and GIS technology will make the task easier and user friendly. Measurable and cost-effective: The index will be a measurable one. Land managers and decision makers must know how to use it and interpret the results. Capacity building programme can make the land managers competent to handle different techniques and also to measure the index. However, it will be cost-effective to quantify using experts from outside (Convention on Biological Diversity, 1999; Riley, 2000). Changes in the ecological attributes can be accommodated with the approach. Policy relevance: For an effective index of ecological integrity in the regional scale, a set of indicators must reflect policy relevant perspectives. There must be indication for policy relevance, progress towards policy targets, and understandability of the indicators (EEA, 2005). Comprehensive: A useful Regional Index of Ecological Integrity must be comprehensive, considering compositional, structural and functional attributes of the ecosystems (Noss, 1990; Lindenmayer and Franklin, 2002; Müller, 2005). For each candidate metric, the important point(s) to consider in the selection process as listed in Table 2 must be taken into account. Though the traditional approach for environmental management is based on structural attributes, but an effective approach must be linked with functional ecosystem features, such as energy and matter

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Table 1 Comparison of the modified characteristics of a proposed Regional Index of Ecological Integrity (RIEI) with Riley (2000) and Andreasen et al. (2001) proposed indices for landscape level. Riley (2000)

Andreasen et al. (2001)

Our proposal for RIEI

1. Universal 2. Portable 3. Sensitive to changes 4. Simple 5. Inexpensive 6. Adjustable with historical data 7. Wide use

1. Multi-scaled 2. Grounded in natural history 3. Relevant and helpful 4. Flexible 5. Measurable 6. Comprehensive • Composition • Structure • Function

1. Multi-scaled 2. Grounded in history and succession • Natural history of organisms • History of successional attributes and evolution • Conservation importance (e.g. endangered, endemic) • Adaptations to environmental changes 3. Relevant and helpful 4. Simple and flexible 5. Measurable and cost-effective 6. Adjustable 7. Policy relevance 8. Comprehensive • Composition • Structure • Function

Table 2 Critical points to evaluate a candidate metric. Candidates/category

Important information for consideration

Components of ecological integrity

Whether the components suited with the basics of ecological systems? e.g. composition, structure, and function

Scale

Is the metric sufficiently addressing the regional aspects? Is temporal scale is sufficient to address historical and ecological attributes?

Comprehensive

Do the components sufficiently represent the vital aspects of regional system, both spatial and temporal?

Relevance

Whether the metrics relevant with the objectives of the assessment?

Usefulness

Are all the components selected as candidate metrics able to represent the vital and important characteristics of regional ecological integrity? They must relate to the science, policy, ethics and belief of the particular region

Aquatic/terrestrial

Does the metric provide information on the linkage between terrestrial and aquatic ecosystems in the region?

Feasibility and measurability

Can the metric be quantified using available scientific technology? Does it cost-efficient and easy to handle, and have sufficient longevity? Whether the baseline data is available (from national, regional or international source) and can store for long-term? Whether some capacity building programme can make the stakeholder competent to use it? Will the relevant agency be able to handle continuous monitoring programme? Is/are the endpoint(s) meet the expectations of the specific objectives?

Ease of interpretation

Are changes and results in the metric value can be interpreted by the public and decision maker?

Adjustability

Whether the measurability and endpoints of the metric is adjustable and meaningful in interpretation and evaluation with the adjacent ecoregions?

flow and cycling, storage and losses, information dynamics and resource utilization (Müller et al., 2000). Some of the important indicators which can be considered as representatives of these three components are listed below: 1. Composition: Compositional features represent an ecosystem and a set of vital species can give the scenario of that particular area (Noss, 1990). These metrics focus on biological entities like keystone species that dominate ecosystem processes, sensitive species or threatened species. Selection of candidates is highly context-dependent and must vary from region to region. Furthermore, expert biological knowledge and natural history is important for appropriate judgment (Andreasen et al., 2001; Karr and Chu, 1999). Focal species, indicator species, keystone species, exotic or invasive species, and endangered species can be considered as suitable compositional metrics. 2. Structure: Indices based on habitat measurements (structural) might be more cost-effective than the indices based on organisms (compositional). Several landscape metrics can be used to quantify the structural condition in a region. For example total number of patches, mean patch size, mean patch distance, patch size standard deviation, shape index, and proximity index. The functional relationship among patches defined as landscape

connectivity (Taylor et al., 1993; With et al., 1997). It can be measured in two aspects; structural and functional. The former is based on landscape structure and the later considers organism’s behavioral responses to landscape elements. In addition to the basic spatial pattern, structural metrics might also include percent cover and habitat available for indicator species (Andreasen et al., 2001), which can make a relationship with structural and compositional variables. 3. Function: Functional metrics concerns vital processes, such as biogeochemical, hydrological, ecological and evolutionary processes. They may be classified as biotic, abiotic or combination of biotic and abiotic processes. Some potential functional candidate metrics for regional scale are listed as follows: (i) Biotic: competition, biomagnifications, predation. (ii) Abiotic: soil erosion, soil acidification, land forms. (iii) Combined: succession, biodegradation, decomposition. 4.2. The work plan for developing an effective RIEI Selection of appropriate indices in the development of a Regional Index of Ecological Integrity requires very careful judgment. We consider four major components to evaluate the ecological integrity that are mostly relevant to structural, functional and compositional aspects of the regional ecological system

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Table 3 Suggested set of metrics with useful tools for the development of RIEI. Major components

Set of metrics/indicators

Scale/useful tools

Fragmentation

Number of patches, patch density, total patch area, mean patch size, patch perimeter, mean patch core area, patch shape index, mean nearest neighbour Percent representation of the surrounding ecosystem, percent representation of total area of same ecosystem in the region Forest interior, riparian area, buffer zones, gap within patches, relative road density Sensitivity of soil erosion, land desertification, soil acidification, salinity or siltation Structural connectivity: patch cohesion, proximity index, nearest neighbour distance, fractal dimension Functional connectivity: graph theory, using connectivity model

FRAGSTATS,a V-LATEb in GIS platform

Representativeness

Ecosystem sensitivity

Landscape connectivity

a b

According to the World Conservation Union

Landscape Analysis in ArcGIS 9x USLE, USDA, UNEP proposed methodology Landscape indices in FRAGSTATS or V-LATE According to Bunn et al. (2000) and Ferrari et al. (2007); using ArcRstats or CS22 software

FRAGSTATS (http://www.umass.edu/landeco/research/fragstats/fragstats.html). V-LATE (http://arcscripts.esri.com/details.asp?dbid=13898).

(see Fig. 4). Moreover, remotely sensed data or other digitized data which have been proposed as candidate indices have a better chance to suit with the index due to its broader aspects and availability (Andreasen et al., 2001). Furthermore, ability of relatively fast acquisition, accommodating capability of different layers (e.g. climate, geology, physiography, soil, hydrology, vegetation, and biogeography), outstanding performance in visualization, suitability to cope with GIS technology make this data very useful to solve the complexity of such ecological analyses (Chuvieco, 1999; Chowdhury, 2006). Table 3 represents some selected indices that can be used to quantify different aspects of the proposed RIEI. However, one needs to select a set of indices directly relevant to a target region and the set may differ according to differences in regional ecological compositions. Moreover, the following sub-sections describe in more detail how we can use the major components in analyzing different indices. 4.2.1. Fragmentation Fragmentation measures, both spatially and temporally, the extent of anthropogenic pressures on the regional natural condition. There are several indices that measure the degree of landscape fragmentation, such as density of patch, mean patch size, patch perimeter and patch shape index. Fragmentation of landscape can be measured from the value 0 to 1, where 0 indicates the land-

Fig. 4. Schematic flow chart showing different components chosen for the development of Regional Index of Ecological Integrity (RIEI). Fragmentation, ecosystem sensitivity, connectivity and representativeness of protected areas are the component indicators of stressors on the regional ecosystems. Ecological integrity of a region can be measured using different indices related to their qualitative and quantitative attributes.

scape has not been destroyed at all and 1 implies the landscape has been totally destroyed. The degree of fragmentation which can be calculated for landscape is as follows (Penghua et al., 2007):

FR = MPS

Nf − 1 Nc

where FR is the fragmentation for the landscape, MPS is the mean patch size which is calculated by the average area of all patches divided by the minimum patch area in landscapes. Nf stands for the total number of patches in the landscape; and Nc is the ratio of the whole area of landscape to the area of minimum patch size. Furthermore, effect of fragmentation on key species can also be measured and eventually accommodate with the candidate metrics. So, fragmentation indices represent structural phenomenon as well as their relations with the compositional attributes too.

4.2.2. Representativeness Representativeness of protected areas will quantify the status and effectiveness of policy, management and planning for sustainable environment and development [emphasized by EEA (2005) for policy relevance]. To quantify the representativeness of protected areas, an ecosystem map of the target region has to be developed. An ecosystem map is a vital part to represent ecological pattern and processes in a region which enables the use of ecosystem occurrences as a robust spatial unit of analysis for variety of applications, including conservation planning, climate change effects, resource management, and analyses of the economic value of ecosystem benefits (see http://rmgsc.cr.usgs.gov/ecosystems/, and also http://www.earthobservations.org/). So, the ecosystems will be geospatially delineated as facets of the landscape generated through biophysical stratification by bioclimate, biogeography, lithology, landforms, surface moisture, and land cover. Once an ecosystem map is being developed, it has to be overlaid with a digitized map of the protected areas of that region. The ecosystem composition of the protected areas and their representation of coverage as a percentage compared to the total area of related ecosystem in the study area can be calculated. Generally, if 10% of a certain ecosystem is protected, it can be considered as wellrepresented in the protected area system (World Conservation Union, 1992; World Resources Institute, 1994; Noss, 1996). Representativeness of protected areas must be compared, both spatially and temporally, with the degree of fragmentation. Otherwise, current representation status of an ecosystem may show higher percent of representation than the previous status of that ecosystem in a fragmented landscape. Representativeness of a protected

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area can be calculated with following formula: Pi × 100 Le

RPi =

where RPi stands for representativeness of protected area i; Pi represents the total area for the protected area i and Le is the total land area of that particular ecosystem subjected to the representation by the protected area i. This index basically represents policy and management perspectives. Moreover, it also represents the feature and dynamics of the regional ecosystems. 4.2.3. Ecosystem sensitivity Ecosystem sensitivity indices can be another source of indicators which can be interpreted with the landscape pattern and may give a considerable explanation of anthropogenic disturbance regime. Sensitivity to land desertification, soil erosion, soil acidification, salinity or siltation can be measured. For example, sensitivity to soil erosion can be measured based on the Universal Soil Loss Equation (USLE), which is an erosion model that predicts soil loss as a function of soil erodibility (K-factor), as well as topographic, rainfall, vegetation cover, and management factor. A soil erosion classification needs to be developed for the region and then the sensitivity of soil erosion (weathering) can be measured through the following (Penghua et al., 2007): SWi =

n  Bij j=1

Bi

Sij

where SWi stands for the sensitivity to soil erosion of the landscape type i; Bij represents the area that landscape i distributes on j sensitive level of soil erosion. Bj is the whole area of landscape type i; Sij is the weight of landscape type j to i sensitive level of soil erosion; j is the sensitive level of soil erosion; i is landscape type; and n is the total number of landscape types. On the other hand, some landscape indices such as, forest interior, riparian area, buffer zones, relative road density can also be measured and after weighting they can be used for the sensitivity analysis (Canaan Valley Institute, 2001). A sensitivity map can be developed through using Analytic Hierarchy Process (AHP) in the GIS platform (weighted overlay) which can give a spatially explicit feature of the ecosystem sensitivity (Saaty and Vargas, 1991). So, this component represents vital processes and also their relation with compositional and structural attributes in the regional scale. 4.2.4. Landscape connectivity Landscape connectivity is ‘the degree to which the landscape facilitates or impedes movement of organisms among resource patches’ (Taylor et al., 1993). Natural and human induced damage to habitats can alter spatial relationships between habitat patches. Therefore, the susceptibility of spatial relationships to patch loss and associated degradation to connectivity is an important factor determining ecological integrity of a region (Matisziw and Murray, 2009). However, landscape connectivity can be measured in two aspects, structural and functional. • Structural connectivity: Structural connectivity is often measured using the Euclidian shortest distance. It has been successfully used in the quantification of simple to complex landscape connectivity features (Moilanen and Nieminen, 2002). The following formula can be used for the measurement of structural connectivity (Moilanen and Hanski, 2001)

S=

n  

Aci

i=1

j= / 1

D(dij , ˛)Abj

where Ai is the area of patch i (=1, 2, . . . , n); parameters b and c represent scale area, patch i being the target and patch j being the source of migration; D(dij , ˛) scales the effect of distance on migration rate; dij is the distance between patches i and j and ˛ is a vector of species-specific parameters describing the dispersal ability of the species. This formula can be modified according to the need of assessment (Kindlmann and Burel, 2008). • Functional connectivity: Functional connectivity considers the behavioral responses of organisms to landscape pattern. It can be measured as mean probability of moving between pairs of patches that is known as emigration and dispersal success (Tischendorf and Fahrig, 2000; Taylor et al., 2006). Graph theory often is used to quantify functional connectivity (Bunn et al., 2000; Ferrari et al., 2007). It is a multidirectional graph representation that allows for multiple pathways between nodes (patches) and, which may be a realistic depiction of connectivity with reference to the actual movement of wildlife. Ti =

n  k=1

Wik Ak

A  i

A¯

where Ti represents functional connectivity; Ai is the area of the focal patch (or fragment), Ak is the area of a single patch in the study area; A is the mean area of all patches, and Wik describes the interaction between patch k and all other patches i. Moreover, recently developed software, for example, ArcRstats (http://www.nicholas.duke.edu/geospatial) or Conefor Sensinode 2.2 (CS22) (http://www.conefor.udl.es/) can be used to quantify connectivity using a graph theoretical approach. They identify the least cost paths among core habitat areas from which network centrality metrics can be calculated. ArcRstats (version 0.7, released 27.06.2006) required two inputs, habitat patches (e.g. core areas) and a cumulative distance surface (see Urban and Keitt, 2001; Goetz et al., 2009). 4.3. Calculation and final index Developing a single value through integrating all the metrics is the final step of the proposed RIEI. This step is a vital part of the whole index which will simply represent the feature of the regional ecosystem. All indices from the major components (e.g. fragmentation, landscape connectivity) are weighted in the final calculation. Principal component analysis (PCA) can be a suitable tool for reduction of metrics and also to reduce the redundant information in order to avoid unnecessary complexity and redundancy. However, in some cases semantic analysis or choice of indicators according to the expert judgment may be applied. Multivariate analysis is another statistical tool to choose suitable metrics from the list. Indices from each major component will be measured and they will be calculated for individual indices. For example, indices from the landscape connectivity would be weighted and standardized from which suitable indices will be selected (considering Table 2). Then, they would be used to calculate connectivity index, which is scored between 0 and 4 (where 4 is excellent and 0 is the worst). Similarly, other components can be developed into an index for their own category. Degrading components can be calibrated such a way that scoring is reversed to weight in a similar fashion [e.g. from 0 to 4 (where 4 is not degraded and 0 indicates fully degraded)]. However, selection of metrics based on expert opinion, experts are human being, and their decisions are influenced by personal experience, institution, heuristics and bias (Kampichler et al., 2010). Uncertainty or vagueness of rating the importance of every possible explanatory variable might motivate the experts to choose a high number of indicators. A sensitivity analysis can be used to assess

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the sensitivity of the index to the weights used for each index. The sensitivity can be checked by calculating a coefficient of sensitivity (CS) based on the standard economic concept of elasticity, that is, the percentage change in the output for a given percentage change in an input (Mansfield, 1985). The calculation of the coefficient of sensitivity is as follows (see Abdullah and Nakagoshi, 2007): CS =

(XIj − XIi )/XIi (WTjk − WTik )/WTik

where XI is a measured index value, WT is the adjusted value of weight given to the respective metrics, i and j represent the initial and adjusted values, respectively, and k represents the indicator component. If the value of coefficient of sensitivity is less than one, then the XI value (output) is considered to be robust to changes in the weight of metric component (input). Finally, Regional Index of Ecological Integrity can be measured using the formula: RIEI = ˛ FI + ˇ SI +  CI + ı RI where RIEI is the Regional Index of Ecological Integrity, FI, SI, CI, and RI stand for fragmentation index, sensitivity index, connectivity index and representativeness index, respectively; ˛, ˇ, , and ı represent weight for the values. Weighting for such value will derive from their relative importance to ecological integrity and also related to the management objectives of the region. The remaining variables of the final index are simple and straightforward, and understandable having no math anxiety. The sensitivity of RIEI to the weight component can be measured using the same procedure as the single indices of fragmentation, sensitivity, connectivity and representativeness. However, spatially explicit mapping of each component index and a final index might give a real feature which may be easily interpreted by the land managers and stakeholders. Moreover, an additional representation like pie chart or other graphic features can be constructed if it needs to demonstrate the exact scenario in some cases, especially in the case of the vital individual features. For example, data from endangered species or eutrophication of water body. Testing of metrics is a necessary step in choosing appropriate methodology for the index. Moreover, it is also necessary to evaluate its performance through applying the index in a number of sites, ranging from undisturbed to degraded areas. Simplest method is required for analysis, because the index must communicate to decision makers and the public. Though, there might be a risk of avoidance of some potential problems of the real-world application which may not be calculated through the proposed sophisticated approach. These problems can be solved through a testing for a period of time. Critical questions of utility and feasibility can be answered and limitations can be sorted. And, through solving such questions, limitations and problems, the index could get reliability and applicability. 5. Conclusions Ecosystem will continue to degrade unless we take proper policy measures for preventive and restorative strategies to achieve the health and integrity of regional ecosystems (Rio Declaration, 1992; Franklin, 1993; Rapport et al., 1998). Under such circumstances an index of ecological integrity, which needs to be developed, might give a valuable support to the policy makers for sustainable management of natural resources (Andreasen et al., 2001; Müller, 2005; Niemeijr and de Groot, 2008). In fact, during the last decade, there have been substantial scientific advances in the development of indices, with an attempt to measure the ecosystem integrity (Borja et al., 2009). Though many of them are successful for some small scale area, but no index of ecological integrity exists up to date for

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a regional level comprising both terrestrial and aquatic ecosystems (Slocombe, 1992; Andreasen et al., 2001; Borja et al., 2009). The proposed Regional Index of Ecological Integrity (RIEI) is an attempt to develop a suitable approach for assessing ecological condition at regional level. This effort tries to include a possible set of indicators to make the index multi-scaled, but useful, simple, flexible and comprehensive on the one hand, and it will be measurable, cost-effective and policy relevant on the other. In this context, understandability of the approach was emphasized as land managers and policy makers will be the primary customers (Andreasen et al., 2001). In this approach, indicators or indices from four major components, i.e. fragmentation, ecosystem sensitivity, landscape connectivity and representativeness of protected areas were suggested. The combination of landscape pattern (e.g. fragmentation and connectivity) and ecosystem sensitivity is able to represent ecological vulnerability of a region (Penghua et al., 2007). So, we do not measure vulnerability separately which will simplify the approach and will make the process comparatively easier to measure. The proposed indicators are representing the compositional, structural and functional attributes of ecosystem; moreover, representativeness of protected areas represents policy and management perspectives. Most of the indicator/indices under the proposed components should be based on remotely sensed satellite data and other digitized data which might be suitable for the development of the index (Andreasen et al., 2001; Ortega et al., 2004; Borja et al., 2009). Modern and available techniques can be used to quantify the indices (for example, GIS softwares). Furthermore, a number of newly required indices can be incorporated in the index due to changing circumstances, which may make the index measurable, flexible and adjustable. Representativeness of protected area will cover the policy relevance features which constitute an important part of indicators for sustainable management (EEA, 2005). The proposed index is combining both landscape and environmental attributes for ecological integrity thus it is broader than many other approaches for biological integrity (also suggested by Stevenson and Pan, 1999; Zampella et al., 2006). Practically, it must be mentioned that all the various components of an ecosystems cannot be encapsulated within a single index (Price et al., 1999). So, there is a risk of ignoring some important issues in the region through this methodology. Because the index emphasizes the simplicity and understandability as it must communicate to decision makers and the public. However, carefulness in selection procedure may minimize the risk. On the other hand, it is expected that the index should be bulletproof, and the index would have to be site, problem and scale-specific (Andreasen et al., 2001). The task is difficult as there are a lot of expectations to meet for such an index (Karr and Chu, 1999) but at the same time growing demand for it may make the task effective (Barbour et al., 2000). In conclusion, this paper has outlined a possible way to develop a Regional Index of Ecological Integrity which might be a useful tool for decision makers for sustainable management of natural resources at regional level. The challenges are also worth considering so as to develop such an index because there is no value of the approach without involvement of land managers and stakeholders. They may need instant result, though there is a lack of consensus on the concept and feasibility of a regional index. But, the beginning of research and testing on such index may attract them and also may build a basis of reliability of RIEI. A continuous effort will be needed to validate and incorporate changing features in the index. A long-lasting research work on ecological attributes can give a positive input to the index. Local universities have the opportunity and capability to play a positive role in such perspectives. Therefore, this effort may be included in similar efforts in sustainable management of natural resources, particularly in the regional context.

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Acknowledgements The authors would like to thank two anonymous reviewers for many constructive comments, suggestions and insights to improve this manuscript. We are very much thankful to the Ministry of Science, Technology and Innovation (MOSTI), Malaysia for their support and funding for this research work through the research project: Science Fund 04-01-02 SF-0378 entitled “Landscape Ecological Assessment of Protected Areas in Peninsular Malaysia for Sustainable Management Planning”.

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