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Environmental Impact Assessment Review
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A multi-scale qualitative approach to assess the impact of urbanization on naturalhabitats and their connectivity
Rocco Scolozzi a,⁎, Davide Geneletti b
a Sustainable Agro-ecosystems and Bioresources Department, IASMA Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, 38010 San Michele all'Adige, (TN), Italyb Department of Civil and Environmental Engineering, University of Trento, Trento, Italy
Habitat loss and fragmentation are often concurrent to land conversion and urbanization. Simple applicationof GIS-based landscape pattern indicators may be not sufficient to support meaningful biodiversity impact as-sessment. A review of the literature reveals that habitat definition and habitat fragmentation are frequentlyinadequately considered in environmental assessment, notwithstanding the increasing number of tools andapproaches reported in the landscape ecology literature.This paper presents an approach for assessing impacts on habitats on a local scale, where availability of spe-cies data is often limited, developed for an alpine valley in northern Italy. The perspective of the methodologyis multiple scale and species-oriented, and provides both qualitative and quantitative definitions of impactsignificance. A qualitative decision model is used to assess ecological values in order to support land-use de-cisions at the local level. Building on recent studies in the same region, the methodology integrates variousapproaches, such as landscape graphs, object-oriented rule-based habitat assessment and expert knowledge.The results provide insights into future habitat loss and fragmentation caused by land-use changes, and aimat supporting decision-making in planning and suggesting possible ecological compensation.
Natural habitat loss and fragmentation are common consequencesof land development. They pose a significant threat to animal andplant species diversity (Raatikainen, et al., 2007; Saunders, et al.,1991) then they may affect biodiversity composition, structure (orga-nization in time and space) and key ecological functions (Noss, 1990).A vast literature supports the view that the persistence of speciespopulations in a landscape largely depends on the size and spatialconfiguration (e.g., connectivity) of good quality habitats (Opdamand Steingrover, 2008; Opdam, et al., 2006). It is therefore of para-mount importance that decisions concerning land-use conversion,such as those typically taken in spatial planning, take into accountthe spatial patterns of ecosystems in order to allow species to survivelocally.
A number of indices for assessing spatial landscape configurationhave been developed (e.g. Arnot, et al., 2004; McGarigal and Marks,1995; Wagner and Fortin, 2005; Weiers, et al., 2004), but they areoften couched in general terms to describe the overall ecological qualityof a landscape, and disregard the fact that different species have differ-ent scale-dependent responses to landscape characteristics. Landscape
indices that fail to account for this scale-dependent variation have littleor no ecological significance (Tischendorf, 2001; Vos, et al., 2001) andalthough theymay usefully represent differences in landscape patterns,they do not offer consistently valid measures of species habitat qualitythat could be used to support spatial planning (Opdam, et al., 2001).
Local-level spatial planning is often affected by a lack of compre-hensive biodiversity assessment (Geneletti, 2008), especially insmall municipalities where ecological data may be scarce or inade-quate. Although habitat loss and fragmentation are widely addressedin the landscape ecology and conservation biology literature, plan-ning and environmental assessment practices do not take full advan-tage of recent methods and approaches, particularly where conceptssuch as habitat potential and habitat connectivity are concerned.
This paper presents a method based on multiple scale and species-specific assessment of habitat quality to support land-use decisions atthe local level. The method is applied to an Alpine environment inItaly, where, in addition to natural barriers (e.g. cliffs, rivers), valleyfloors are often exposed to sprawling urban growth and infrastruc-ture development which constitute physical constraints to animalflows. The paper is organized as follows: Section 2 presents a reviewof recent literature on habitat loss and fragmentation, and consider-ation of them in environmental assessments; in Section 3 we describethe study area and in Section 4 the method proposal; Section 5 re-ports the specific application results; and finally, Section 6 presentsa discussion and some conclusions are reported in Section 7.
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2. A short review of the literature on habitat loss and fragmentation
2.1. Concepts and methods
The term habitat is often used loosely as equivalent to “native vege-tation” or other natural or semi-natural land cover types. Consequently,impacts on habitat are often measured in terms of loss of vegetationcover. However, habitat is more correctly defined as “the resourcesand conditions present in an area that produce occupancy for a particu-lar species or species assemblage” (Hall et al., 1997). Habitat is thereforea species-specific concept. The extent of a particular type of land coverin itself rarely reflects the extent of a suitable habitat for a given species(e.g. Gentile and De Bernardi, 2004). Native vegetation cover may notbe particularly relevant, especially in European regions where manylandscapes have a prolonged history of human modification and man-agement (Lindenmayer and Fischer, 2006) and where several speciesnow depend upon these human-made landscapes, especially in theAlps (Chemini and Rizolli, 2003).
According to the above definition of habitat, habitat loss can comeabout not only through land cover changes but also through decreas-ing availability of resources and accessibility to refuges or breedingareas (Eikaas andMcIntosh, 2006). Besides, clearing native vegetationdoes not necessarily cause habitat loss as some species may adapt tothe cleared areas. Analogously, the expansion of native vegetationdoes not in itself contribute to biodiversity. For example, the expan-sion of forests over declining pastures in Alpine regions is actuallydiminishing plant species diversity and negatively affecting non-woodland species (Laiolo, et al., 2004). Moreover, species populationsmay disappear due to agriculture intensification (Benton, et al., 2003;Schmitz, et al., 2007) or to over- or under-grazing practices (Diemer,et al., 2001; Schmitz, et al., 2003).
For these reasons, it has been suggested that species indicators beused in landscape planning (e.g. Bani, et al., 2002; Bianconi, et al.,2003; Padoa-Schioppa, et al., 2006) and in landscape ecology assess-ment (e.g. Mörtberg et al., 2008; Vos, et al., 2001). A commonmethodfor selecting species to provide indicators of biodiversity impact isbased on the concept of focal species (Lambeck, 1997), these beingthe most area-sensitive, dispersal-limited, resource-limited and eco-logical process-limited taxa in a landscape. The idea is that a land-scape designed and managed to meet the needs of focal species willalso meet the needs of many other species. Habitat models for focalspecies can be used to address issues relating to biodiversity impactassessments at the landscape level (Fernandes, 2000; Gontier et al.,2006, 2010; Scolozzi and Geneletti, 2011). A wide range of methodsexist for modeling the distribution of species and their habitats (fora short review see Gontier, et al., 2010).
Habitat fragmentation is defined as a landscape-scale process in-volving both habitat loss and the break-up of habitat (Fahrig, 2003).It implies various effects on habitat pattern, such as: a reduction inthe extent of the habitat, an increase in the number of habitatpatches, a decrease in the size of habitat patches, increasing isolationof patches. The spatial pattern of fragmentation sets in train a series ofnegative ecological effects, in particular those involving impedimentsto fluxes of organisms (Hanski, 1994), materials (sediment, nutrients,pollen, seeds) and energy, which are essential to ecosystem dynamicsand integrity (Lundberg and Moberg, 2003).
As with fragmentation, connectivity has become a commonlyused, but also controversial, term. Connectivity is a primary processwhich influences ecosystem function and the distribution, abundanceand persistence of all biota (Lindenmayer, et al., 2007). Landscapeconnectivity plays an important role in the persistence of manyplant and animal populations in the face of global climate changeand resulting shifts in and restructuring of species distributions(Vos, et al., 2008). According to Taylor et al. (1993), landscape con-nectivity is the degree to which the landscape facilitates or impedesspecies movement between resource patches. Hence, connectivity
cannot be captured simply by an index based on landscape geometricparameters, but should be organism-centered, i.e. based on theorganism's perception of and interaction with the structure andheterogeneity of the landscape (Bender, et al., 1998; Hansen andUrban, 1992; Lindenmayer, et al., 2002). Assessment of connectivityand related impacts requires information on the movement ofspecies, their responses to landscape structure, their movementrates through different landscape elements, their dispersal range,and their mortality during dispersal, and boundary interactions.
A variety of methods for assessing habitat connectivity (Calabreseand Fagan, 2004) and fragmentation (e.g. Jaeger, 2000) have beenproposed. Connectivity is appraised from either a structural or a func-tional perspective (Tischendorf and Fahrig, 2000), and while structur-al connectivity is equated with habitat contiguity and is measured byanalyzing landscape structure independently of any of the attributesof the organism(s) of interest (e.g. Collinge, 1996), functional connec-tivity explicitly considers the behavioral responses of an organism tothe various landscape elements (patches and boundaries). Structuralconnectivity is easier to assess than functional connectivity becauseit can be computed with relatively few data using landscape analysistools commonly supported by Geographical Information Systems(GIS). However, the use of one-fits-all landscape metrics appears tobe too simplistic for assessing effects on species movement(Adriaensen, et al., 2003; Fall, et al., 2007; Winfree, et al., 2005).Many studies do not clearly state whether they are dealing withstructural or functional connectivity, while others confuse patch iso-lation with connectivity resulting, in some cases, in misleading orambiguous conclusions (as reported in Quine and Watts, 2009;Tischendorf and Fahrig, 2000).
It is important to analyze the potential flows of species dispersaland to assess the possible bottlenecks or gaps that land-use changemay cause (Adriaensen, et al., 2003). For example, Lindenmayer andFischer (2007) reported two studies, conducted simultaneously inthe same geographical area, that led to conflicting results becauseone focused on patches of remnant vegetation (structural connectiv-ity) but ignored vegetation structure (providing habitat functions andfunctional connectivity).
A familiar approach to measuring connectivity which incorporatesgeographical information as well as species behavioral considerationsis ‘least-cost path’ modeling (Adriaensen, et al., 2003). The basic idea,as proposed by Knaapen et al. (1992), consists in assigning a frictionvalue to a landscape unit (grid cell) based on the extent to which itfacilitates or hinders species movement. This value is used to calcu-late the connectivity between source and target cells by adding to-gether the values of all the cells crossed (Adriaensen, et al., 2003).
A recent and increasingly used approach applies graph theory tolandscape connectivity analysis (Fall, et al., 2007; Minor and Urban,2008; Pascual-Hortal and Saura, 2006; Urban and Keitt, 2001). Agraph, also referred to as a “landscape graph”, is a set of nodes con-nected by edges, where nodes represent habitat sites and edges theconnections between habitats. Edges are binary (connected or uncon-nected) or may contain additional information about the level of con-nectivity (e.g. flux of individuals between nodes or probability ofconnection). Landscape graphs have proven to be very effective incommunicating habitat connectivity information in a comprehensibleand comprehensive manner (Fall, et al., 2007). Spatial graph-basedanalyses may help to identify key features playing a critical role inthe persistence of species populations and hence support selectionof habitat reserves (Pascual-Hortal and Saura, 2007). For further de-tails and a more formal discussion of landscape graphs, the reader isdirected to two recent comprehensive reviews (Galpern, et al.,2011; Urban, et al., 2009).
Habitat connectivity/fragmentation is also addressed by spatially-explicit models of metapopulation in the Population Viability Analysis(Foppen, et al., 2000). In this approach a habitat network is con-sidered “functioning” if the potential flow of individuals between
patches has a pre-defined degree of probability of sustaining a persis-tent (i.e. viable) metapopulation (Opdam and van den Brink, 2007).However, when applied to environmental assessment processes,problems arise with the existing tools as they are complex, case-specific and dependent on species distribution data (Calabrese andFagan, 2004; Urban, 2005). After reviewing empirical and modelingstudies, in order to resolve these limitations Verboom et al. (2001)formulated an operational approach based on “persistence norms”for “key populations”, these being the number of reproductive units(pairs of individuals, families or territories, depending on the species)required for a viable population (in such case the local population isconsidered being a “key population”). The reproductive unit can becounted as the minimum group of animals able to reproduce itself,it is used as measure for habitat carrying capacity (Vos et al., 2001)or habitat potential (Scolozzi and Geneletti, 2011).
2.2. Applications in the literature on environmental assessment
In their review of the landscape ecology literature, Lindenmayerand Fischer (2007) found that many measurements of habitat frag-mentation are limited and biased by a binary interpretation of land-scape (habitat/non-habitat), where habitat is defined as an area ofnative vegetation (generally woodland). They call this the “humanperspective”, whereas for assessing fragmentation they consider a“species perspective” to be more appropriate; this is based on amore articulated interpretation of landscape (habitat functioning/non-functioning for species X) and where fragmentation refers tothe possibility species dispersal between habitats.
Inspired by their account, we reviewed the literature specificallydealing with environmental assessment by searching papers men-tioning “habitat connectivity”, “habitat loss” or “habitat fragmenta-tion” published in the period 1997–2010. The search was restrictedto leading journals in environmental assessment in the ISI Web ofScience and Springer-Link databases, namely: Environmental ImpactAssessment Review, Impact Assessment and Project Appraisal, Jour-nal of Environmental Management, Journal of Environmental As-sessment Policy and Management, Environmental Monitoring andAssessment, Journal of Environmental Planning and Management,Environmental Management.
We found 90 papers that matched these criteria (Annex 1). Ofthese, 64 (71%) simply mentioned or claimed to consider habitat lossand fragmentation. 18 (20%) based their assessment on connectivity/fragmentation of vegetated areas without referring to any particularspecies. A few studies (10 papers, 11%) defined and measured habitatfragmentation from a species perspective based on selected speciesand their habitat requirements or dispersal traits. Four studies (4%)used this information to assess possible impacts caused by land-usechanges, while five cases dealt with the impact of project developmenton habitat connectivity.
We can conclude that, generally, there is still a gap between a rap-idly increasing research field, developing progressively more sophis-ticated approaches and tools, and the applications in environmentalmanagement and assessment. These seem to suffer a number ofdrawbacks, according to Lindenmayer and Fischer claims. Besides,some of drawbacks have already been pointed out in past reviews.Three reviews (Geneletti, 2002; Mandelik, et al., 2005; Treweek,1996) report similar issues, although they stress different aspects ofthem: lack of adequate ecological data, failure to address properlythe study area and spatial scale focusing instead on project site orprotected areas, neglecting to define impact significance. In our re-view, we found confirmation that fragmentation is rarely predictedand quantified, and specifically that the possibility of assessing the re-duction in quality of remnant fragmented areas is to a large extentlimited by the size of the study area and by poor structuring of theevaluation frameworks, e.g. unstated objectives, criteria not clearlylinked to objectives, value judgments expressed without providing
the rationale behind them (Geneletti, 2003). Conversely, determina-tion of impact significance should be fully substantiated by thresh-olds, criteria and decision motivations, and supported by qualitativeand quantitative data, clear evidence and reasoned arguments. More-over, the process of determining significance should be adaptable tochanging knowledge, and should facilitate collaboration with inter-ested and affected parties (Lawrence, 2007).
Summarizing, threemain issues need to be addressed in order thathabitat and connectivity impact assessment be reliable:
a) Fragmentation-sensitive species (focal species) to be determinedand used as a reference for fragmentation assessment;
b) Adoption of a suitable spatial scale for the input data and results,in line with the decision-making scale and the specific ecologicalcharacteristics of the studied landscape;
c) Ecological values to be assessed by considering the role that differ-ent sites in the whole landscape play in sustaining local biodiver-sity rather than focusing on limited areas (e.g. project area orprotected areas).
In the remainder of the paper, we propose a methodology aimedat including and integrating all the above issues, and report its appli-cation to a study area in an Italian alpine valley.
3. Study area
The study area is a section of the Valsugana Valley (Trentino re-gion, Italy) located in the Southern Limestone Alps and characterizedby morphological bottlenecks (plane area transectb2 km). The origi-nal river meadows, forests and marshes have been replaced by inten-sive fruit and vine cultivation. Small nature reserves without spatialconnectivity are scattered throughout the valley floor. Given that asignificant portion of alpine biodiversity is found at lower elevations(Sergio and Pedrini, 2007), in common with other Alpine valley floorsthe lower part of this valley is of interest with respect to general bio-diversity conservation. Natural morphological constraints on move-ment (e.g. steep, rocky mountainsides) create unique areas withregard to habitat and dispersal requirements (e.g. seasonal move-ment along elevation gradients, annual migrations between suitablehabitats) for several species in the valley floors.
In this paper we focus on the municipality of Roncegno (Fig. 1),where the proposed method was tested in the support to the local spa-tial plan that was being drawn up. Roncegno is a small municipality ofless than 3000 inhabitants, with a land surface area of 38.05 km², mak-ing it typical of the many small Alpine municipalities. In fact, there are4547 municipalities with less than 2500 inhabitants within the AlpineConvention area, accounting for over three-quarters of the existing5954 municipalities (Alpine Convention, 2011).
4. Methods
The aim of the method is to predict the impact of land-use changeson local biodiversity using habitat requirements of focal species asreference. It combines and extends two previous studies conductedin the same area aimed at assessing habitat potential (Scolozzi andGeneletti, 2011) and at mapping the remnant habitat connections(Scolozzi and Geneletti, 2009, 2012). The first study assessed habitatvalues by estimating the number of reproductive units of selectedspecies, a restricted set assumed to be focal, and by the use of spatialrules and high resolution vegetation maps (reference scale 1:5000,minimum mapping area 50 m2). The second study assessed connec-tivity values by analyzing landscape barriers for the same speciesset using landscape graphs and expert judgments. In both cases spa-tial information was structured in a hierarchical multiple scale frame-work (Fig. 2) and included landscape objects such as patch, patchmosaic, unit, and network of units. The patches are characterized bya homogeneous type of vegetation, so that patch mosaics represent
Fig. 1. Location of the study area and main land use types displayed over a digital elevation model.
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vegetation mosaics. Definition of units and network units relied onidentification of landscape barriers, which can be either artificial(built-up areas, roads) or natural (rivers, cliffs). Within this frame-work, the ecological properties of one level explicitly affect the prop-erties of another through spatial and non-spatial rules embedded inthe assessment of habitat potential and connectivity. For instance, achange in land cover may change the habitat potential of a patch,and in turn that of a patch mosaic, which consequently may affectthe functioning of a habitat network. Conversely, a change to a unitarea may change the habitat potential of all the patches within it.
Unit Network
Unit
Patch
PatchMosaic
Fig. 2. Spatial objects structured into hierarchical levels (bars at the side indicate spatial screlationships between the different levels mutually affecting ecological properties).
The results of the two above-mentioned assessments are aggre-gated into a general “ecological value” using a qualitative hierarchicalmulti-attribute decision model (MADM) (Bohanec and Zupan, 2004).This is intended to generalize species-specific information to a gener-al ecological evaluation and to examine the potential role each patchplays in supporting the local biodiversity.
More precisely, all the patches are characterized by attributes relatedspecies-specific habitat potential and habitat network functioning. Oper-ationally, in the vectormap eachpatch-polygon has its own ID identifyingit as a unique patch, but also specific IDs designating the patchmosaic, the
Connections
Natural Barrier
Artificial Barrier
10 m
100 m
1000 m
10 km
ales; the black arrows exemplify species dispersal, the gray arrows indicate qualitative
Table 1Qualitative order scale used in the decision-tree classification.
Attributes Nominal scales
Ecological value Very high; high; medium; low; negligibleConnectivity value High; medium; low; negligibleHabitat value High; medium; low; negligibleValue for grassland/woodland/wetland
Breeding; survival, dispersal; unsuitable;hostile
Habitat value for species A/B/C/D/E/F Breeding; survival, dispersal; unsuitable;hostile
unit and unit network to which it belongs. On the basis of these IDs, eachpatch-polygon “inherits” properties from higher-level objects throughdecision rules, embodied in the tailoredMADM. The application of all de-cision rules to vector maps was performed by converting them into SQLqueries and using ArcGIS software (ESRI 2008).
MADMs are widely used for classifying or evaluating options, wherea complex decision is decomposed into smaller and less complex sub-problems, consisting in a multi-level hierarchic structure of objectives,criteria, subcriteria and alternatives. In general, they consist of attri-butes, Xi, representing decision sub-problems, and utility functions,also named value functions, Fi, which connect attributes across differentlevels and integrate them in the evaluation or classification (Fig. 3).Along the levels, the several basic attributes are aggregated, through autility function, into aggregate attributes, then into the final overallevaluation.MADMs are part of thewider framework of themulticriteriadecision analysis (MDCA), see Mendoza and Martins (2006) for a re-view. The methods relying on quantitative decision models use contin-uous attributes and utility functions, usually defined in terms ofattribute weights (Zopounidis and Doumpos, 2002). Conversely, quali-tative decision models are characterized by the use of qualitative attri-butes, whose scales are discrete and typically consist of words ratherthan numbers, and employment of utility functions represented by de-cision rules, i.e. if-then rules, rather than numerical formulae (Bohanecand Rajkovi, 1999; Bohanec et al., 2008).
The attributes and utility functions (i.e. aggregation rules) were de-fined and structured using the DEXi software (Bohanec, 2011). Fig. 3shows the hierarchical structure of attributes in the form of a decisiontree, and Table 1 reports qualitative ordinal scales. In our specific case,basic attributes and the associated scale refer to the different functionseach landscape patchmay provide for the selected specieswith in termsof suitable breeding area, feeding resources, and dispersal opportunity(as resulted from Scolozzi and Geneletti, 2011) or in terms of connectiv-ity for the habitat networks (from Scolozzi and Geneletti, 2012). Thecontribution of all these specific attributes is considered in the overallevaluation as definition of ecological values.
More precisely, the selected species are representative of speciesgroups related to the threemain habitat types in the study area, namely:Rana synk. esculenta (Edible frog) and Calopteryx virgo (Damselfly) forwetlands and local water ecosystems; Erinaceus europaeus (Hedgehog)and Lanius collurio (Red-backed Shrike) for grasslands and mixed agri-cultural areas; Moscardinus avellanarius (Hazel dormouse) and Sittaeuropaea (Nuthatch) for woodlands. These species were selected con-sidering their ecological traits and considered as focal species. Beingarea-sensitive and fragmentation-sensitive, and with home-rangescomparable with extension of study area, they were assumed to be
Fig. 3. Decision tree of the multi-qualitative attribute model to define an ecological val
consistent references for the assessment of landscape ecological pro-cesses at local (municipal) scale.
The species-specific basic attributes are aggregated into values forrelated habitat types. As an example, Fig. 4 presents the rules appliedto grassland habitat values. The habitat values are subsequently aggre-gated into general habitat values. These represent the ecological valuein terms of the habitat potential of each landscape patch, independentof species or particular habitat/land cover type. Both these aggregationsfollow a “maximum rule” in which the final attribute is defined by thebest case, assuming equal importance of species and of habitats(Fig. 4). This relative importance expresses an approximation of criteriaweights used in MCDA. In qualitative multi-attribute models there isnatively no room for weights since attributes are symbolic/qualitativeand utility functions are defined by decision rules (see Bohanec, 2011).
In assessing (habitat) connectivity values, “functioning” refers tohabitat patches within a habitat network suitable for sustaining a vi-able species metapopulation, i.e. arbitrarily at least three key popula-tions; “fairly functioning” refers to a habitat network suitable forsustaining at least one key population; “not functioning” refers to ahabitat network unsuitable for hosting a breeding population (seeScolozzi and Geneletti, 2012). It is worth noting that connectivity in-formation is associated with a given polygon patch but relies on infor-mation from an (upper) habitat network level.
The decisions/aggregations concerning connectivity values werebased on a particular kind of “maximum rule”, which considers twoterms together, i.e. the Y attribute is defined according to the two bestcases (in the discrete scales). This more complex approach is justifiedgiven that Units, composed ofmosaics of patches (see Fig. 2),may includemore than onehabitat type. Thus, the highest values are attributed toUnitnetworks functioning for more than one selected species, hence morethan one habitat type. This rule can be represented as follows:
ue considering habitat and connectivity values, as resulted from previous studies.
Fig. 4. Example of aggregation rules at first level.
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• IF Functioning for one selected species AND (Functioning OR Fairlyfunctioning for another species) THEN High
• IF Fairly functioning for at least two species OR Functioning for onlyone species THEN Medium
• IF Fairly functioning for only one species THEN Low• OTHERWISE Negligible.
A complete, extended list of rules is reported in Annex 2.Finally, the Ecological value is obtained by aggregating Habitat
value and Connectivity value according to the rules summarized inthe matrix below (Table 2). These rules assume that two Patcheswith the same “inner” properties, for instance, the same vegetationstructure and composition, have different ecological values depend-ing on connectivity within the habitat network to which they belong.
The rule matrix is not symmetrical as the definitions assign aslightly higher priority to habitat quality. This is because the function-ing of the area is held to be more important for biodiversity conserva-tion (Fahrig, 2003). Moreover, high quality habitat areas may harborimportant ecological processes and may contribute to conservationof other components of local biodiversity (not just animal species).Connectivity, as measured here, is a crucial process mainly for terres-trial fauna.
5. Results
With respect to habitat value, the territory of Roncegno provideswoodland and grassland species with large tracts of highly valuable
Table 2If-then rules for classification of the overall ecological value considering the qualitativeassessment of connectivity and habitat.
Connectivity
High Medium Low Negligible
Habitat High Very high Very high High HighMedium High Medium Medium LowLow Medium Low Negligible NegligibleNegligible Low Low Negligible Negligible
areas, while habitat patches for wetland species are fewer and small-er. Assuming all three habitat types to have equal importance, theecological value is generally high (Fig. 5). It is worth noting that dif-ferent approaches may be taken, for instance assigning priority to aspecific habitat type (e.g. wetland) or to multi-functional areas (e.g.those patches capable of supporting both wetland and grassland se-lected species).
According to the connectivity assessment, the municipal territoryappears to be moderately fragmented, especially in the lower partof valley, mainly because of significant barriers such as roads (Fig. 6,right).
To illustrate the usefulness of the approach in supporting urbanplanning, it was used to assess the impact of a proposed urban devel-opment (see Fig. 7). Fig. 8 shows a sub-window of the value maps inpre- and post-development conditions, which were simulated bychanging the land use to urban area and re-running the evaluation.Expected changes in habitat values and connectivity were detectedand quantified (Figs. 8 and 9 show only the changes in habitatvalues).
The results of the impact assessment were used to suggest case-specific ecological compensation measures, aimed at achieving no-net-loss of habitat values and connectivity. The value maps (Fig. 6)show that the main issue for the planning area is habitat connectivity,and so the ecological compensation measures were focused on en-hancing species dispersal through the grassland matrix and sparseurban settlements. These measures consisted in planting and/ormaintaining lines of trees, small semi-natural woodlands and nativespecies-rich hedgerows along the secondary road network withinthe grassland (mixed crop) areas and within the new urban area, aswell restoring a small mixed riparian floodplain woodland along theexisting watercourses (center-right in Fig. 7). These compensationmeasures focus on grassland species, predicted to be the most nega-tively affected group, as shown in Fig. 9.
6. Discussion
The qualitative multi-attribute evaluation presented here inte-grates and completes two previous original approaches, forming acomprehensive procedure to support impact assessment of land-usechanges at a local level. The approach was designed to predict theconsequences for biodiversity brought about by land-use changesusing readily available data sets. It overcomes the limitations of im-pact assessments based on land cover information and geometriclandscape indices which are often ambiguous in terms of the extentto which local biodiversity is affected. The proposed approach allowsthe species perspective to be taken into account, as claimed byLindenmayer and Fischer (2007), and species-specific assessmentsto be aggregated, while maintaining the transparency and adaptabil-ity of the procedure during the evaluation process. Each value defini-tion is substantiated by ecological arguments, at the same time easyto explain and communicate. Moreover, the method provides infor-mation in varying degrees of detail (see Figs. 6 and 7), from overallecological quality (ecological value) to habitat potential and species-specific connectivity/fragmentation. In the course of the same assess-ment process it is possible to make use of general value information,for instance during a preliminary analysis, as well as more detailed in-formation which could indicate, for example, type and site or restora-tion and compensation measures.
The two dimensions of the ecological value can also be combinedto identify the potential ecological role played by each site (patch)within the landscape (Table 2) in local biodiversity. Intuitively, theareas with high connectivity and habitat value are potential sourcesof local biodiversity (Table 3). Areas with lower connectivity value,but high habitat value (e.g. isolated but functioning habitat areas)may provide stepping stones. Conversely, areas with relatively lowhabitat value but which are well connected may still form ecological
corridors. For each expected ecological role a proper design option(Table 4) may be pursued in an initial design phase of the spatialplanning process. Obviously, the categories shown in Tables 3 and 4
Fig. 6. Habitat values (left) and connectivity values (right) for a portion of the study a
are merely indicative and should be interpreted as fuzzy and generic.However, the loss of information precision in this schema is justifiedby the gain in clarity for decision-makers (often non-ecologists).
rea, as example some attributes are reported for a site (white selected polygon).
Fig. 7. Planning scenario and ecological compensation proposals.
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The approach is not intended to model ecological processes on alandscape scale, nor to model species distribution using metapopula-tion spatial patterns or dynamics. This would require a wealth ofbaseline data on species distribution and population sizes, whichare rarely available for spatial planning at the local level. In this par-ticular case study, qualitative (word scale) and quantitative assess-ments (number of reproductive units potentially hosted by thearea), integrated with visualization of habitat connectivity through
Fig. 8. Impacts of an urbanization plan on habitat and connectivity: be
the spatial graph, facilitated reliable comprehension of ecologicalvalues and impacts by non-ecologist members of the urban planningcommittee. In effect, the method overcomes some of the previously-mentioned limits of many landscape indices in providing operationalindications for planning. Forman (1995, p.9) noted that “a minimumof three linkages must be known. The element is linked to the: (1)encompassing element at the next higher level; (2) nearby elementsat the same scale; and (3) component elements at the next lower
fore (on the left) and after (on the right) proposed development.
level”. In the methodology presented here, these three levels aretaken into account and used to differentiate ecological values in thestudy area.
The rule-based approach proposed here is a transparent and adap-tive procedure, with the scope, for instance, for different definitions ofweights. In our case we applied a maximum rule, meaning that differ-ent species or habitat types have equal importance. In other cases, ag-gregation may assign greater importance to a particular habitat type,for example, one that is more vulnerable and rare in the area of con-cern. In an ideal application of the methodology, these rules would bediscussed with local experts and the outcomes shared with plannersand stakeholders.
Specifically, the methodology was found to be helpful in providingsupport in different phases of landscape planning. By highlighting thepotential role of vegetated areas for local biodiversity, ecologicalcharacterization facilitates understanding of the ecology of the land-scape studied, which could be useful in a screening phase. Moreover,application of the method allows the potential impact of planningscenarios on habitats (Fig. 8) to be anticipated and site-specific mea-sures and types of ecological compensation to be figured out, valuablein the design phase of landscape planning (see Table 2). Dependingon the specific threat to the conservation of local biodiversity (e.g.,connectivity or the extent of suitable habitat areas), different mea-sures may be proposed to reduce or compensate for the impacts,such as increasing and improving suitable habitat areas or linkingpresent patches by “de-fragmentation”. We have shown that theprocedure presented provides operational and value-based infor-mation for appraising the significance of impacts on habitat infuture scenarios. These results are useful for re-defining and im-proving proposals for land-use change. Although habitat loss andfragmentation impacts are often correlated, distinguishing betweenthem may pave the way for more targeted strategies for compensa-tion or mitigation.
The same ecological characterization of a municipal territorymay support the screening and scoping phases of EIA. For each
Table 3Potential ecological role of areas according to their habitat and connectivity values.
combination of habitat/connectivity value at different sites it ispossible to define specific warning levels, which could be adoptedas a kind of checklist for assessment of the impact on the ecolog-ical structure of a landscape. This checklist is, of course, meant as aguide for further analyses, in accordance with the different ecolog-ical roles of a project site, and does not replace the need for moreprecise evaluations.
7. Conclusion
According to our short review, habitat loss and habitat fragmenta-tion are frequently inadequately considered in environmental impactassessments. Few studies take a species perspective in fragmentationanalyses; fragmentation is rarely quantified and, more specifically, as-sessment of the reduction in quality of remnant fragmented areas re-mains limited. A reliable impact assessment on habitats and theirconnectivity requires fragmentation-sensitive species to be used asa reference in the analyses, while the spatial scale of these analysesshould be consistent with the spatial planning scale. Moreover,these assessments should provide value-based information to facili-tate decision makers' understanding of ecological values and the po-tential impacts on them.
The proposed qualitative multi-attribute evaluation merges twoprevious studies focused on habitat potential and habitat connectivi-ty, resulting in a procedure aimed at addressing the above-mentioneddrawbacks in impact assessment. Application of the procedure yieldsinformation in varying degrees of detail, which can be used at differ-ent stages of landscape planning, as shown in the specific case of spa-tial planning in Roncegno. Lastly, the proposed method has beenshown to be helpful in integrating different sources of qualitative en-vironmental information and provides a better understanding oflandscape ecology in municipal spatial planning.
Table 4General indications for planning design associated to potential ecological roles of areas.
Values Connectivity
High Medium Low Negligible
Habitat High Conservationpreserve fromurbanization(or compensation)
Human perspective(vegetation/landscape/ecological connectivity)
Specie perspective (specifichabitat connectivity/fragmentation)
Species explicited (bracketswhen species are merely citedand not used)
Environmental Impact AssessmentReview (29)
George (1997) xTollefson and Wipond (1998) xGeorge (1999) xFernandes, 2000 x xAntunes et al. (2001); xCooper and Sheate (2002) xJoão (2002) xTodd (2002) x x xGeneletti et al. (2003) xGeneletti, 2003 xGallardo and Sánchez (2004) xKuo et al. (2005) xAtkinson et al. (2006) xGeneletti (2006) xGontier et al., 2006 x xSöderman (2006) xGontier (2007) x xLawrence, 2007 xMulvihill and Ali (2007) xTherivel and Ross (2007) xGeneletti, 2008 xKing and Pushchak (2008) xSchetke and Haase (2008) xGeneletti and Dawa (2009) xTherivel (2009) xGontier et al., 2010 xGontier et al., 2010 x xMasden et al. (2010) xVillarroya and Puig (2010) x
Impact Assessment and ProjectAppraisal (5)
Prato and Hamed (1999) xBunnumma Swangjang et al.(2004)
x
Söderman (2005) xBrownlie and Botha (2009) xKhera and Kumar (2010) x
Journal of EnvironmentalManagement (20)
Bunn et al. (2000) x xHinsley and Bellamy (2000) X xHirst et al. (2000) xLee et al. (2001) xDramstad et al. (2002) xBayliss et al. (2003) xCooper et al. (2003) xWulf (2003) xMbile et al. (2005) x xBenayas et al. (2006) xRescia et al. (2006) xMörtberg et al. (2008) x xMata et al. (2008) x xMac Nally (2008) x xDi Giulio et al. (2009) xvan Langevelde et al. (2009) x xPetit (2009) xQuine and Watts, 2009 x x (x)Mancebo Quintana et al.(2010)
x x (x)
Teixido et al. (2010) xJournal of EnvironmentalAssessment Policy andManagement (2)
Fernandes, 2000 xPeterson et al. (2010) x
Environmental MonitoringAnd Assessment (13)
Laurance (2000) xMora et al. (2000) xBennett and Milne (2004) xWeber (2004) xAurambout et al. (2005) xLeimgruber et al. (2005) xCakir et al. (2008) xCarranza et al. (2008) xSantos et al. (2008) x xHartig et al. (2009) xGiriraj et al. (2010) xKagalou et al. (2010) xLopez et al. (2010) x
Annex 1. Literature review: habitat loss and fragmentation in environmental assessment.
18 R. Scolozzi, D. Geneletti / Environmental Impact Assessment Review 36 (2012) 9–22
Human perspective(vegetation/landscape/ecological connectivity)
Specie perspective (specifichabitat connectivity/fragmentation)
Species explicited (bracketswhen species are merely citedand not used)
Journal of Environmental Planningand Management (1)
Gill et al. (2010) x
Environmental Management (20) Bogaert et al. (2004) xLepczyk et al. (2004) xTimm et al. (2004) xWarnock and Skeel (2004) xBruggeman et al. (2005) x (Abstract model)Conway and Lathrop (2005) x (x)Hilli and Kuitunen (2005) xBaird et al. (2005) xDuinker and Greig (2006) xMrosovsky (2006) x (x)Quigley and Harper (2006) xRothley (2006) (Hypothetical
landscape model)Esbah (2007) xFreund and Petty (2007) xTrisurat (2007) xWohl et al. (2007) xScrimgeour et al. (2008) xWeber et al. (2008) xBaer et al. (2009) xMamun (2010) x
Annex 2. All decision rules used in the multi-attribute decisionmodel are reported here. Table 1 shows the decision tree with hierarchical
structure of attributes and aggregation rules (see tables below).
Table 1
Table 2
Table 3
Here “complex rules”were used in order to represent their utility functions in a more compact manner than with elementary rules (in our case theywould be 125). Complex rules are obtained by joining several elementary rules, for instance rule 1 says that ifWetland communities is “Breeding” thePatch habitat value is “high” regardless on the values of Grassland and Woodland communities; symbol “>=” stands for “less or equal than”.
Table 4
Table 5
20 R. Scolozzi, D. Geneletti / Environmental Impact Assessment Review 36 (2012) 9–22
Adriaensen F, Chardon JP, De Blust G, Swinnen E, Villalba S, Gulinck H, et al. The appli-cation of ‘least-cost'modelling as a functional landscape model. Landscape UrbanPlann 2003;64(4):233–47.
Alpine Convention. Sustainable rural development and innovation — report on theState of the Alps. Innsbruck. Permanent Secretariat of the Alpine Convention;2011. URL: http://www.alpconv.org/index_en, last access November 2011.
Arnot C, Fisher PF, Wadsworth R, Wellens J. Landscape metrics with ecotones. patternunder uncertainty. Landsc Ecol 2004;19(2):181–95.
Bani L, Baietto M, Bottoni L, Massa R. The use of focal species in designing a habitat net-work for a lowland area of Lombardy, Italy. Conserv Biol 2002;16(3):826–31. M.
Bender DJ, Contreras TA, Fahrig L. Habitat loss and population decline. a meta-analysisof the patch size effect. Ecology 1998;79(2):517–33.
Benton TG, Vickery JA, Wilson JD. Farmland biodiversity. Is habitat heterogeneity thekey? Trends Ecol Evol 2003;18(4):182–8.
Bianconi R, Battisti C, Zapparoli M. Pattern of richness, abundance and idiversity offour interior bird species in hilly landscape in Central Italy. A contribution toassess their sensitivity to habitat fragmentation. J Mediterr Ecol 2003;4(3–4):37–44.
Bohanec M. DEXi: Program for multi-attribute decision making, User’s manual, Version3.03. Ljubljana: Jožef Stefan Institute, 2011.
Bohanec M, Rajkovi V. Multi-attribute decision modeling: industrial applications ofDEX. Informatica 1999;23:487–91.
Bohanec M, Zupan B. A function-decomposition method for development of hierarchi-cal multi-attribute decision models. Decis Support Syst 2004;36:215–33.
Bohanec M, Messéan A, Scatasta S, Angevin F, Griffiths B, Krogh PH, Znidarsic M,Dzeroski S. A qualitative multi-attribute model for economic and ecological assess-ment of genetically modified crops. Ecol Model 2008;215:247–61.
Calabrese JM, Fagan WF. A comparison-shopper's guide to connectivity metrics. FrontEcol Environ 2004;2(10):529–36.
Chemini C, Rizolli A. Land use change and biodiversity conservation in the Alps. J MtEcol 2003;7:1–7.
Collinge SK. Ecological consequences of habitat fragmentation. Implications for land-scape architecture and planning. Landscape Urban Plann 1996;36(1):59–77.
Diemer M, Oetiker K, Billeter R. Abandonment alters community composition and can-opy structure of Swiss calcareous fens. Appl Veg Sci 2001;4(2):237–46.
Eikaas HS, McIntosh AR. Habitat loss through disruption of constrained dispersal net-works. Ecol Appl 2006;16(3):987–98.
Fahrig L. Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst2003;34(1):487–515.
Fall A, Fortin MJ, Manseau M, O'Brien D. Spatial graphs. Principles and applications forhabitat connectivity. Ecosystems 2007;10(3):448–61.
Fernandes JP. Landscape ecology and conservation management—evaluation of alter-natives in a highway EIA process. Environ Impact Assess Rev 2000;20(6):665–80.
Foppen RPB, Chardon JP, Liefveld W. Understanding the role of sink patches in source-sink metapopulations. reed warbler in an agricultural landscape. Conserv Biol2000;14(6):1881–92.
Galpern P, Manseau M, Fall A. Patch-based graphs of landscape connectivity. A guide toconstruction, analysis and application for conservation. Biol Conserv 2011;144(1):44–55.
Geneletti D. Ecological evaluation for environmental impact assessment. Geographicalstudies. Netherlands: Vrije Universiteit Amsterdam; 2002.
Geneletti D. Biodiversity impact assessment of roads. An approach based on ecosystemrarity. Environ Impact Assess Rev 2003;23(3):343–65.
Geneletti D. Incorporating biodiversity assets in spatial planning. Methodological pro-posal and development of a planning support system. Landscape Urban Plann2008;84(3–4):252–65.
Gentile FF, De Bernardi F. Amphibians in a human-dominated landscape. The commu-nity structure is related to habitat features and isolation. Biol Conserv2004;119(2):219–30.
Gontier M, Balfors B, Mörtberg U. Biodiversity in environmental assessment — currentpractice and tools for prediction. Environ Impact Assess Rev 2006;26(3):268–86.
Gontier M, Mörtberg U, Balfors B. Comparing GIS-based habitat models for applicationsin EIA and SEA. Environ Impact Assess Rev 2010;30(1):8-18.
Hall LS, et al. The habitat concept and a plea for standard terminology. Wildlfe SocietyBulletin 1997;25:173–82.
Hansen AJ, Urban DL. Avian response to landscape pattern. The role of species' life his-tories. Landsc Ecol 1992;7(3):163–80.
Hanski I. Patch-occupancy dynamics in fragmented landscapes. Trends Ecol Evol1994;9(4):131–5.
Jaeger JAG. Landscape division, splitting index, and effective mesh size. new measuresof landscape fragmentation. Landsc Ecol 2000;15:115–30.
Knaapen JP, Scheffer M, Harms B. Estimating habitat isolation in landscape plannning.Land Urban Plann 1992;23(1):1-16.
Laiolo P, Rolando A, Valsania V. Responses of birds to the natural re-establishment ofwilderness in montane beechwoods of North-western Italy. Acta Oecologica2004;25(1–2):129–36.
Lambeck RJ. Focal species. A multi-species umbrella for nature conservation. ConservBiol 1997;11(4):849–56.
Lawrence DP. Impact significance determination—pushing the boundaries. Environ Im-pact Assess Rev 2007;27(8):770–88.
Lindenmayer DB, Fischer J. Habitat fragmentation and landscape change. An ecologicaland conservation synthesis. London: Island Press; 2006.
Lindenmayer DB, Fischer J. Tackling the habitat fragmentation panchreston. TrendsEcol Evol 2007;22(3):127–32.
Lindenmayer DB, Cunningham RB, Donnelly CF, Nix H, Lindenmayer BD. Effects of for-est fragmentation on bird assemblages in a novel landscape context. Ecol Monogr2002;72(1):1-18.
Lindenmayer D, Hobbs RJ, Montague-Drake R, Alexandra J, Bennett A, BurgmanM, et al.A checklist for ecological management of landscapes for conservation. Ecol Lett2007;10:1-14.
Lundberg J, Moberg F. Mobile link organisms and ecosystem functioning. implicationsfor ecosystem resilience and management. Ecosystems 2003;6(1):87–98.
Mandelik Y, Dayan T, Feitelson E. Planning for biodiversity. The role of ecological im-pact assessment. Conserv Biol 2005;19(4):1254–61.
22 R. Scolozzi, D. Geneletti / Environmental Impact Assessment Review 36 (2012) 9–22
McGarigal K, Marks BJ. FRAGSTATS. Spatial pattern analysis program for quantifyinglandscape structure. General technical report. Portland, Oregon, USA: USDA ForestService, Pacific Northwest Research Station; 1995.
Mendoza GA, Martins H. Multi-criteria decision analysis in natural resource manage-ment: a critical review of methods and newmodelling paradigms. For Ecol Manage2006;230(1–3):1-22.
Minor ES, Urban DL. A graph-theory framework for evaluating landscape connectivityand conservation planning. Conserv Biol 2008;22:297–307.
Mörtberg UM, Balfors B, Knol WC. Landscape ecological assessment. A tool for integrat-ing biodiversity Minor ES, Urban DL. A Graph-Theory Framework for EvaluatingLandscape Connectivity and Conservation Planning. Conserv Biol 2008;22(2):297–307.
Noss RF. Indicators for monitoring biodiversity. A hierarchical approach. Conserv Biol1990;4(4):355–64.
Opdam P, Steingrover E. Designing metropolitan landscapes for biodiversity. derivingguidelines from metapopulation ecology. Landscape Jrnl 2008;27(1):69–80.
Opdam P, van den Brink A. Incorporating ecological sustainability into landscape plan-ning. Landscape Urban Plann 2007;79:374–84.
Opdam P, Foppen R, Vos C. Bridging the gap between ecology and spatial planning inlandscape ecology. Landsc Ecol 2001;16:767–79.
Opdam P, Steingröver E, Sv Rooij. Ecological networks. A spatial concept for multi-actorplanning of sustainable landscapes. Landscape Urban Plann 2006;75(3–4):322–32.
Padoa-Schioppa E, Baietto M, Massa R, Bottoni L. Bird communities as bioindicators.The focal species concept in agricultural landscapes. Ecol Indic 2006;6(1):83–93.
Pascual-Hortal L, Saura S. Comparison and development of new graph-based landscapeconnectivity indices: towards the priorization of habitat patches and corridors forconservation. Landsc Ecol 2006;21(7):959–67.
Pascual-Hortal L, Saura S. Impact of spatial scale on the identification of critical habitatpatches for the maintenance of landscape connectivity. Landscape Urban Plann2007;83(2–3):176–86.
Quine CP, Watts K. Successful de-fragmentation of woodland by planting in an agricul-tural landscape? An assessment based on landscape indicators. J Environ Manage2009;90(1):251–9.
Raatikainen K, Heikkinen R, Pykälä J. Impacts of local and regional factors on vegetationof boreal semi-natural grasslands. Plant Ecol 2007;189(2):155–73.
Saunders DA, Hobbs RJ, Margules CR. Biological consequences of ecosystem fragmenta-tion. A review. Conserv Biol 1991;5:18–32.
Schmitz MF, De Aranzabal I, Aguilera P, Rescia A, Pineda FD. Relationship betweenlandscape typology and socioeconomic structure. Scenarios of change in Spanishcultural landscapes. Ecol Model 2003;168(3):343–56.
Schmitz MF, Sánchez IA, De Aranzabal I. Influence of management regimes of adjacentland uses on the woody plant richness of hedgerows in Spanish cultural land-scapes. Biol Conserv 2007;135(4):542–54.
Scolozzi R, Geneletti D. An approach to present and to assess landscape functional con-nectivity at local scale. In: Breuste J, Kozovà M, Finka M, editors. Challenges for
Landscape Ecology and Management European IALE Conference; Salzburg (Austria),Bratislava (Slovakia). European Landscapes in TransformationUniversity of Salzburg,Slovak University of Technology in Bratislava and Comenius University in Bratislava;2009. p. 372–8.
Scolozzi R, Geneletti D. Spatial rule-based assessment of habitat potential to predictimpact of land use changes on biodiversity at municipal scale. Environ Manage2011;47(3):368–83.
Scolozzi R, Geneletti D. Assessing habitat connectivity for land-use planning: a methodintegrating landscape graphs and Delphi survey. J Environ Plan Manage 2012.Available on line www.tandfonline.com/doi/abs/10.1080/09640568.2011.628823.
Sergio F, Pedrini P. Biodiversity gradients in the Alps. The overriding importance of el-evation. Biodivers Conserv 2007;16(12):3243–54.
Taylor PD, Fahrig L, Heinen K, Merriam G. Connectivity is a vital element of landscapestructure. Oikos 1993;68(3):571–2.
Tischendorf L. Can landscape indices predict ecological processes consistently? LandscEcol 2001;16(3):235–54.
Tischendorf L, Fahrig L. On the usage and measurement of landscape connectivity.Oikos 2000;90(1):7-19.
Treweek J. Ecology and environmental impact assessment. J Appl Ecol 1996;33(2):191–9.
Urban DL. Modelling ecological processes across scales. Ecology 2005;86(8):1996–2006.Urban D, Keitt T. Landscape connectivity. A graph-theoretic perspective. Ecology
2001;82(5):1204–18.Urban DL, Minor ES, Treml EA, Schick RS. Graph models of habitat mosaics. Ecol Lett
2009;12(3):260–73.Verboom J, Foppen R, Chardon P, Opdam P, Luttikhuizen P. Introducing the key patch
approach for habitat networks with persistent populations. An example for marsh-land birds. Biol Conserv 2001;100:89-101.
Vos CC, Verboom J, Opdam PFM, Ter Braak CJF. Toward ecologically scaled landscapeindices. Am Nat 2001;183(1):24–41.
Vos CC, Berry P, Opdam P, Baveco H, Nijhof B, O'Hanley J, et al. Adapting landscapes toclimate change. Examples of climate-proof ecosystem networks and priority adap-tation zones. J Appl Ecol 2008;45(6):1722–31.
Wagner HH, Fortin M-J. Spatial analysis of landscapes. Concept and statistics. Ecology2005;86(8):1975–87.
Weiers S, Bock M, Wissen M, Rossner G. Mapping and indicator approaches for the as-sessment of habitats at different scales using remote sensing and GIS methods.Landscape Urban Plann 2004;67(1–4):43–65.
Winfree R, Dushoff J, Crone EE, Schultz CB, Budny RV, Williams NM, et al. Testing sim-ple indices of habitat proximity. Am Nat 2005;165:707–17.
Zopounidis C, Doumpos M. Multicriteria classification and sorting methods: a literaturereview. Eur J Oper Res 2002;138:229–46.
