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An integrated method to create habitat suitability models forfragmented landscapes

Valerio Amici a,n, Francesco Geri a, Corrado Battisti b

a Dipartimento di Scienze Ambientali ‘‘G. Sarfatti’’, Universit �a di Siena, via P.A. Mattioli n. 4, c.a.p.: 53100, Siena (SI), Italyb Ufficio Conservazione natura, Servizio Ambiente, Provincia di Roma, via Tiburtina, 691, 00159, Roma

a r t i c l e i n f o

Article history:

Received 24 April 2009

Accepted 6 October 2009

Keywords:

Connectivity

Focal species

Fuzzy set

Habitat conservation

Landscape planning

a b s t r a c t

Given the pervasive influence of human induced habitat fragmentation in ecological processes,

landscape models are a welcome advance. The development of GIS software has allowed a greater use of

these models and of analyses of the relationship between species and habitat variables. Habitat

suitability models are thus theoretical concepts that can be used for planning in fragmented landscapes

and habitat conservation. The most commonly used models are based on single species and on the

assignment of suitability values for some environmental variables. Generally the cartographic basis for

modeling suitability are thematic maps produced by a Boolean logic. In this paper we propose a model

based on a set of focal species and on maps produced by a fuzzy classification method. Focal species,

selected by an expert-based approach, provide a practical way of extending the scope of habitat

suitability models to the conservation of biodiversity at landscape scale. The utilisation of a

classification method that applies a continuity criterion may allow more consideration of the

connectivity of an area because it allows a better detection of ecological gradients within a landscape.

We applied this methodology to the Tuscany region focusing on terrestrial mammals. Performing a

fuzzy classification we produced five land cover maps and through image processing operations we

obtained a suitability model which applies a continuity criterion. The resulting suitability fuzzy model

seems better for the study of connectivity and fragmentation, especially in areas with high spatial

complexity.

& 2009 Elsevier GmbH. All rights reserved.

Introduction

Habitat fragmentation

Human-Induced Habitat Fragmentation (HIHF) is consideredone of the main threats to global biodiversity, and it constitutes aserious threat to biological diversity, contributing to the decreasein number, disruption or extinction of local populations ofsensitive species (Ewers and Didham 2007; Harrison and Bruna1999). The HIHF process causes a reduction of suitable habitats forthese species and, consequently, an increase of their energyrequirements for survival (Hinsley 2000). This is a process thatsummed with other anthropogenic disturbances involves cumu-lative effects on animal and plants populations, affecting eitherthe movement of individuals or their presence, abundance, andpersistence with wider effects at the community and ecosystemlevels (Davies et al. 2001; Soule and Orians 2001; Wilcox andMurphy 1985). The main components of the process of HIHF hasbeen identified as: 1) habitat loss; 2) reduction in area of theremnant fragments (AREA); 3) progressive increase of their

isolation (both for distance and barrier; Biondi et al. 2003; Jaeger2000) (ISOL); and 4) increase of edge effects and anthropogenicdisturbances in remnant patches (EDGE) (Andren 1994; Bennett1999; Fahrig 2003; Hilton-Taylor 2000). It is possible to observethe effects of HIHF at different scales (Collinge 1996). At landscapescale, the process may result in the structuring of patchy mosaicswith remnant fragments of focal habitats (Davies et al. 2001;Fahrig 1997; Forman 1995) and the effects include the disruptionof individual dynamics as a result of changes in habitat shapeand spatial pattern organisation (Forman and Godron 1986;Lindenmayer and Fischer 2006).

The way individuals move in the landscape mosaic is afunction of the individual intrinsic characteristics, includingdispersal capabilities and landscape spatial configuration (Fahrigand Merriam 1994; Hanski 1994). For this reason the strategies forthe conservation of HIHF-sensitive populations cannot be ad-dressed at less than the landscape scale (Battisti 2003). Planningthe maintenance and restoration of connectivity among ecosys-tems and processes is a logical answer to counteract the negativeimpacts of HIHF (Crooks and Sanjayan 2006). Taking into accountthese problems, ecologists have developed a series of conceptssuch as wildlife corridors, landscape links and ecoducts withinthe theoretical framework of landscape ecology (Boitani et al.2007).

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doi:10.1016/j.jnc.2009.10.002

n Corresponding author. Tel.: +39 0577235415; fax: +39 0577232896.

E-mail addresses: valerio.amici@tele2.it, valerio.amici@unisi.it (V. Amici).

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Focal species and expert-based methods of selection

The use of certain species such as ecological indicators(surrogates for assessing the integrity, diversity and vulnerabilityof ecosystems; Andelman and Fagan 2000; Soule and Orians2001) depends on a number of characteristics that must bedefined a priori. The idea of indicator species is based on theobservation that some species are more susceptible to ecologicalchanges than others (Landres et al. 1988). A focal species isidentified as the one showing the most critical requirements interm of spatial requirements or regime disturbance metrics(Lambeck 1997) and a set of focal species may cover a wide rangeof habitat and landscape conditions (Bani et al. 2006).

Some authors, for instance Hess and King (2002), used theDelphi method (Linstone and Turoff 1975) for the selection offocal species in environmental planning, hence obtaining theinformation on the species to be selected by a panel of experts.This approach is useful when uncertainty is high and time islimited. The selected species will allow, for a specific geographicalarea, the creation of suitability models that may help to define thespecific strategies in landscape planning (Hess and King 2002).The findings from these models should be implemented tomitigate the impact induced by HIHF process through a set ofactions (i.e. the increase of connectivity and size in area of theremnant fragments, the increase of habitat suitability of thelandscape matrix, the mitigation of disturbances and edge effect;Bennett 1999), and can be used for monitoring the adoptedmeasures over time.

HIHF-sensitive species might be identified by using ecologicaltraits that may indirectly determine the intrinsic sensitivity ofspecies to habitat fragmentation. In previous approaches, severalauthors have proposed that potentially HIHF-sensitive speciesshould show one or more of the following characteristics: 1) berare (in terms of relative abundance); 2) require habitats of largesuitable size area; 3) be subject to natural demographic fluctua-tions; 4) show a low reproductive potential; 5) have a lowdispersal ability capacity; 6) show a high ecological specialisation;7) show an utilisation of the elements of landscape mosaic limitedto a few habitat types (Bright 1993; Henle et al. 2004). Ewers andDidham (2006) produced a set of diagrams showing generalresponses of species (in terms of categories of sensitivity: high;intermediate; and low) to each of the five components of the HIHFthat they selected: 1) reduction in fragment area; 2) decrease ofthe distance from the edge of the fragment; 3) morphologicalincrease of the complexity of the fragment; 4) increase of thedegree of isolation; 5) increase of the morphological andstructural contrast between matrix and fragment.

This response may be obtained, indirectly, from knowledge offive ecological traits of each species, namely trophic level,dispersal capacity, body size, niche breadth, rarity (in terms ofnumerical abundance). Therefore, if the ecological traits of aspecies are known, on a simplified nominal scale we can obtain anevaluation of its sensitivity (high, medium, low) to each of themacro-components of HIHF (Ewers and Didham 2006).

We decided, following an expert-based approach, to reduce thefive components of fragmentation listed by these authors to thethree main macro-components (see Bennett 1999): AREA (corre-sponding to 1 component of Ewers and Didham 2006), ISOL(component 4), and EDGE (components 2, 3 and 5). Wesubsequently built an evaluation matrix with qualitative evalua-tions (respectively, high, medium, and low) represented by scoreswhere: 1=low sensitivity; 2=medium sensitivity; 3=high sensi-tivity. Value 0 was assigned in the absence of response to process(Table 1).

Following the above-mentioned approach, and knowingthe ecology of a species, it is possible to obtain an indirect

approximate indication of the potential sensitivity of a species toeach of three macro-components of HIHF by adding the valuesobtained for each of five ecological traits considered. Knowing therange of the values (5–15 for macro-component AREA and ISOL,2–6 for EDGE), we provided a set of threshold values beyondwhich the species can be considered as a Fragmentation SensitiveFocal Species (410 for the AREA and ISOL, 45 for EDGE).

Habitat suitability models

We define ‘habitat suitability’ as the ability of a pixel orpolygon to support survival and reproduction of a focal species(NAU 2007). Survival and reproduction are species attributesstrongly linked to HIHF-sensitivity of the species (Crooks andSanjayan 2006). Habitat suitability models are based on func-tional relationships between individual species and habitatvariables on a variable suitability index scale (Larson et al.2003). Habitat suitability index scores are usually calculated usinga mathematical formula representing hypothesised relationshipsamong the individual suitability indices. Wildlife-habitat relation-ships may be supported by empirical data, expert opinion, or both(Larson et al. 2003). The approach may be inductive or deductive;the deductive method (also known as deterministic method) usesspatial data and specific ecological and biological knowledge ofeach entity analysed to determine the corresponding ecologicalrequirements (Guisan and Zimmermann 2000). The inductivemethod does not use suitability attributions but observations inthe field to determine the optimal intervals of environmentalparameters corresponding to the positive presence of the species(Glenz et al. 2001). Modelling habitat suitability appears to be anincreasingly important tool for use in planning and conservation.Indeed, these models, elaborated for a set of selected species onthe basis of known affinities with environmental parameters(generally land cover categories), are tools for predicting potentialpresence of species and the degree of physical connectivity amongsuitable habitat patches in a study area (Battisti 2003; Brown et al.2000). Therefore, these models may be used in analyses regardingHIHF issues, with particular focus on habitat size and quality.Maps produced through this process can identify areas at differentsuitability levels; they have been utilised in many studies toprovide a synoptic view of habitat suitability for specific speciesas well as assess suitability habitats for species assemblages(Bellamy et al. 1996). Furthermore, these maps could contribute toenvironmental planning and nature conservation at the landscapescale (Boitani et al. 2002).

Fuzzy set approach

Mapping is generally achieved through the application of aconventional statistical classification which allocates each imagepixel to a class (Foody 1996). A polygon or a pixel can be describedonly as a single category applying a Boolean membership in theinteger set {0, 1} and the degree to which it is mixed cannot bedifferentiated (Rocchini and Ricotta 2007).

Most of the conservation plans rely on classified maps butthese may be inappropriate for mixed pixels, which contain two ormore classes, and a fuzzy classification approach is better (Foody1996). Fuzzy set theory retains the uncertainty information ofeach class by taking into account the gradual change frommembership to non-membership (Gopal and Woodcock 1994). Afuzzy set is a set whose elements have degrees of membership; anelement of a fuzzy set can be full member (100% membership) or apartial member (between 0% and 100% membership). In this waythe membership value assigned to an element is no longer

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restricted to just two values, but can be 0, 1 or any value in-between (Zadeh 1978).

The mathematical function which defines the degree of anelement’s membership in a fuzzy set is called membershipfunction. The fuzzy membership function assigns for each entity(a polygon or a pixel) a membership level in the range [0, 1]expressing the possibility that a given entity (a polygon or a pixel)belongs to a thematic map class (Zadeh 1978). Analysing thelandscape connectivity, the higher performance of fuzzy land useclassification, thanks to the continuity criterion, seems to better fitecological gradients within a landscape (Comber et al. 2005).

The aim of this paper is to propose a model based on a set offocal species and on maps produced by a fuzzy classificationmethod. The focal species, selected by an expert-based approach,provide a practical way of extending the scope of habitatsuitability models to the conservation of biodiversity at landscapescale. The utilisation of a classification method that applies a

continuity criterion may allow for better consideration of theconnectivity of an area because it allows a better detection ofecological gradients within a landscape, providing an usefulsupport to landscape planning and conservation.

Methods

Study area

The study area is the entire territory of the Tuscany region(Italy, 421–441 North latitude, 91–121 East longitude, WGS84), atypical Mediterranean ecosystem, including forests at variousaltitudinal locations. Forests vary from the evergreen Mediterra-nean forests dominated by Quercus ilex, along the coastlines, to theFagus sylvatica and Abies alba forests of mountain sites. Corineland-cover data (Bossard et al. 2000) show a surface territory of

Table 1Sensitivity values to each component of habitat fragmentation assigned to terrestrial mammals of Tuscany (Agnelli 1998) by experts on the basis of the five selected

ecological traits (TL: trophic level; DA: dispersal ability; BS: body size; NB: niche breadth; RA: rarity). The bold indicates the focal species that were selected. AREA:

reduction in size of area of the remnant fragments; ISOL: progressive increase of their isolation (both for distance and barrier; Jaeger 2000; Biondi et al. 2003); EDGE

increase of edge effect and anthropogenic disturbances in remnants.

Macro-categories Population size Species AREA ISOL EDGE

TL DA BS NB RA tot TL DA BS NB RA tot TL DA BS NB RA tot

AGR 10-100 Apodemus flavicollis 1 1 1 2 1 6 1 2 3 2 1 9 2 2 2 2 2 10

Apodemus sylvaticus 1 1 1 2 1 6 1 2 3 2 1 9 2 2 2 2 2 10

Micromys minutus 1 2 1 2 1 7 1 1 3 2 1 8 2 1 2 2 2 9

Microtus arvalis 1 2 1 2 1 7 1 2 3 2 1 9 2 2 2 2 2 10

100-1000 Lepus europaeus 1 3 2 2 2 10 1 1 2 2 2 8 2 3 2 2 2 11

Oryctolagus cuniculus 1 1 1 1 3 7 1 1 1 3 1 7 2 3 2 1 2 10

41000

MOS Arvicola terrestris 1 1 1 2 2 7 1 2 3 2 2 10 2 2 2 2 2 10

Crocidura leucodon 2 3 1 2 2 10 2 3 3 2 2 12 2 1 2 2 2 9

Crocidura suaveolens 2 3 1 2 2 10 2 3 3 2 2 12 2 1 2 2 2 9

Microtus multiplex 1 2 1 2 2 8 1 2 3 2 2 10 2 2 2 2 2 10

Microtus nivalis 1 2 1 2 2 8 1 2 3 2 2 10 2 2 2 2 2 10

Microtus savii 1 3 1 1 1 7 1 3 3 1 1 9 2 1 2 1 2 8

Myodes glareolus 1 1 1 2 2 7 1 2 1 2 2 8 2 1 2 2 2 9

Sorex araneus 2 3 1 2 1 9 1 3 3 2 1 10 2 1 2 2 2 9

Sorex minutus 2 3 1 2 2 10 2 3 3 2 2 12 2 1 2 2 2 9

Sorex samniticus 2 3 1 2 2 10 2 3 3 2 2 12 2 1 2 2 2 9

Suncus etruscus 2 3 1 2 2 10 2 3 3 2 2 12 2 1 2 2 2 9

Talpa europaea 2 2 2 2 1 9 2 2 2 2 1 9 2 2 2 2 2 10

100-1000 Erinaceus europaeus 2 1 1 2 1 7 2 2 3 2 1 10 2 2 2 2 2 10

Marmota marmota 1 1 2 2 2 8 1 2 2 2 2 9 2 2 2 2 2 10

Martes foina 3 2 2 2 2 11 3 2 2 2 2 11 2 2 2 2 2 10

Meles meles 3 3 2 2 1 11 3 1 2 2 3 11 2 3 2 2 2 11

Mustela nivalis 3 1 2 3 2 11 3 2 2 3 2 12 2 2 2 3 2 11

Mustela putorius 3 1 2 3 3 12 3 2 2 3 3 13 2 2 2 3 2 11

Sus scrofa 1 3 3 1 1 9 1 1 3 1 1 7 2 3 2 1 2 10

41000 Vulpes vulpes 3 3 2 1 1 10 3 1 2 1 1 8 2 3 2 1 2 10

FOR 10-100 Muscardinus avellanarius 2 3 1 3 2 11 2 3 3 3 2 13 2 2 2 2 2 10

100-1000 Eliomys quercinus 2 3 1 3 2 11 2 3 3 3 2 13 2 2 2 2 2 10

Glis glis 2 3 1 3 2 11 2 3 3 3 2 13 2 2 2 3 2 11

Hystrix cristata 1 3 2 1 1 8 1 1 2 1 1 6 2 2 2 1 2 9

Martes martes 3 3 2 3 3 14 3 1 2 3 3 12 2 3 2 3 2 12

Sciurus vulgaris 2 2 2 2 2 10 2 2 2 2 2 10 2 2 2 2 2 10

41000 Canis lupus 3 3 3 2 3 14 3 1 1 2 3 10 2 3 2 2 2 11

Capreolus capreolus 1 3 3 2 2 11 1 1 1 2 2 7 2 3 2 2 2 11

Cervus elaphus 1 3 3 2 3 12 1 1 1 2 3 8 2 3 2 2 2 11

Felix silvestris 3 3 2 3 3 14 3 1 2 3 3 12 2 3 2 3 2 12

WET 10-100 Neomys anomalus 2 3 1 3 3 12 2 3 1 3 3 12 2 1 2 3 2 10

Neomys fodiens 2 3 1 3 3 12 2 3 1 3 3 12 2 1 2 3 2 10

100-1000 Lutra lutra 3 2 2 3 3 13 3 2 2 3 3 13 2 2 2 3 2 11

41000

ANT 10-100 Mus domesticus 1 3 1 1 1 7 1 1 3 1 1 7 2 3 2 1 2 10

100-1000 Rattus norvegicus 1 3 1 1 1 7 1 1 3 1 1 7 2 3 2 1 2 10

Rattus rattus 1 3 1 1 1 7 1 1 3 1 1 7 2 3 2 1 2 10

41000

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about 19,720 km2 of which 44% is covered by forests, while theagriculture area covers about the 46%. The agriculture types thattake up the larger surface area include intensive non-irrigatedarable land alternated with traditional agroecosystems, whilebroad-leaved forests represent the major natural class. Thetopography varies from the plain areas near the coast line andaround the principal river valleys to the hilly and mountainouszones towards the Apennine chain. From the orographic point ofview the region is covered approximately 2/3 by hilly areas, 1/5 bymountains and only 1/10 by plains and valleys. The climate rangesfrom typically Mediterranean to temperate cold following thealtitudinal and latitudinal gradients and the distance from the sea(Rapetti and Vittorini 1995).

Selection of focal species

In this study, we focus on terrestrial mammals because theyconstitute a group of high interest to study landscape-scaleprocesses in the context of HIHF (see Bright 1993).

To select the focal species we consider some ecologicalattributes that determine indirectly their sensitivity to HIHF.Using the diagrams proposed by Ewers and Didham (2006) and byconsulting a group of experts on mammal fauna, we traced thesensitivity of terrestrial mammals of Tuscany (high, medium, low)back to each of the components of HIHF (Bennett 1999). Then webuilt an evaluation matrix dividing the species (based on thecheck list obtained from Agnelli 1998) into the macro-category ofhabitat types they inhabit (inland wetlands, forest ecosystems,agro-ecosystems, mosaic ecosystems, anthropogenic ecosystems)

and into their territory or population size (10–100 km2, 100–1000 km2, 41000 km2).

In this phase of species selection we defined only five macro-categories of habitat types. Indeed, a fine- grained segregation ofspecies would create a difficulty in the objective definition ofclasses of focal species. Hence we changed the term ‘‘habitat type’’into ‘‘macro-categories’’. Then, we transformed both quantitativeand qualitative evaluations used by Ewers and Didham (2006) forassigning the potential response of species to each of the threecomponents of HIHF. The assigned values are: 0=‘‘absence ofresponse to process’’; 1=‘low sensitivity’; 2=‘‘intermediate sensi-tivity’’; and 3=‘‘high sensitivity’’. In order to state, on the basis ofEwers and Didham’s diagrams, the range of variation of possiblevalues (5–15 for macro-component ‘‘reduction in surface habitat’’and ‘‘increase of the degree of isolation’’, 6–12 for ‘‘increase of theedge effect/matrix disturbance’’), was provided a conventionalvalue beyond which the species can be considered focal sensitive(Z10 for all three components).

Fuzzy classification

An ortho-Landsat ETM+ image (path 192, row 030, acquisitiondate June 20, 2000; spatial resolution 30 metres) was acquired.Bands 1–5 and 7 were used; band 6 was not considered due to themuch larger pixel size than the other bands. Classification wasbased on Corine Land Cover level 1 (Bossard et al. 2000). Table 3shows the class descriptions. In order to perform the land coverfuzzy classification, we applied a supervised approach by selectingknown training sites belonging to the five land cover classes

Table 3Land cover classes used for classification. Description and comparison with Corine land cover 2000 classes.

Fuzzy Land Use Classes Description Corine land use classes

1. Urban fabric Buildings, transport network and artificially surfaced areas without vegetation

occupy most of the area, which also contain non-linear areas of vegetation and

bare soil.

111, 112, 121, 122, 123, 124, 131, 132, 133, 141,

142

2. Agricultural area Permanent and non-permanent crops, pasture, areas planted with fruit trees and

vines occupy the most of the area, which also contain interspersed natural areas.

211, 212, 213, 221, 222, 223, 231, 241, 242, 243,

321

3. Woodland Vegetation formations composed principally of coniferous species, broad-leaved

species and mixed species occupy most of the area.

311, 312, 313, 323

4. Shrubland Vegetation with low and closer cover, dominated by shrubs, bushes and maquis

occupy most of the area, which also contain transitional woodland / shrub areas.

222, 322 , 323 , 324, 333

5. Wetland and water Inland and maritime wetlands, natural or artificial water courses and water

bodies.

411, 421, 422 , 511

Table 2Sensitivity values to each land cover class were assigned for the selected focal species. The mean of suitability was calculated for the entire set of selected species in order to

obtain a total value of suitability.

Species 1. Urban fabric 2. Agricultural area 3. Woodland 4. Shrubland 5. Wetland and water

Canis lupus 0 1 3 1 0

Eliomys quercinus 0 1 3 3 0

Felix silvestris 0 0 3 1 0

Glis glis 1 1 3 3 0

Lutra lutra 0 1 3 2 3

Martes foina 1 1 3 2 0

Martes martes 0 0 3 1 0

Meles meles 1 1 3 2 0

Muscardinus avellanarius 0 1 3 3 0

Mustela nivalis 1 1 3 1 0

Mustela putorius 0 1 2 1 3

Neomys anomalus 0 1 2 2 3

Neomys fodiens 0 1 2 2 3

Sciurus vulgaris 1 0 3 3 0

Suitability (average) 0.35 0.78 2.78 1.92 0.85

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(Eastman 2006). The digitalisation of training sites was based onfield data acquired in previous research in Tuscany (see e.g.Chiarucci et al. 2008a; Chiarucci et al. 2008b). In order to create astatistical characterisation of each class, we extracted the spectralsignature of all bands used. The spectral reflectance pattern ofeach band helped to separate the different features because thefraction of energy reflected at a particular wavelength varies fordifferent features. A spectral signature is a unique reflectancevalue in a specific part of the spectrum (Lillesand and Kiefer1999). In order to statistically compare the training sites, themeans of reflectance values of each band were calculated. Theclassifier chosen in this study is based on Bayes Theorem forconditional probability (Lee 1989) and it is used to link a-priori

known presence probabilities of a land cover class with theprobabilities of occurrence, and consequently to assign condi-tional probabilities (Guisan and Zimmermann 2000).

The input prior probabilities of occurrence of each land coverclass were based on covering data extracted from Corine land-cover 2000 (Bossard et al. 2000). Each map showed thea-posteriori membership probability of each pixel to the consid-ered land cover class, according to Bayes Theorem (Eastman 2006;Richards and Jia 1999; Strahler 1980):

pðh9eÞ ¼pðe9hÞ�pðhÞ

Pipðe9hiÞ�pðhiÞ

where p(h9e)=the probability of the hypothesis being true giventhe evidence (posterior probability); p(e9h)=the probability offinding that evidence given the hypothesis being true; p(h)=theprobability of the hypothesis being true regardless of the evidence(prior probability).

In order to assess the correspondence between the Booleancategories identified by Corine Land Cover mapping and theaverage of fuzzy membership values, we created a contingencymatrix (Table 4).

An high agreement between Boolean and fuzzy classification isrelated to near to 1 fuzzy membership values inside cells alongthe main diagonal of the matrix (Tsumoto 2009).

Habitat suitability model

The procedure for construction of habitat suitability models foreach selected focal species has followed the conceptual processproposed by Boitani et al. (2002). For each land use class weassigned a suitability value obtained using expert knowledge foreach species considered. The evaluation scale of suitability wasdeveloped in a range of values between 0 and 3. For each land useclass we calculated the mean of suitability for the entire set ofselected species in order to obtain a total value of suitability(Table 2). Through image processing operations, suitability valueshave been corrected with the value of pixel membership to eachland use class, according to the continuous classification adopted(Heuvelink and Burrough 1993). Therefore, for each pixel the

value of membership to the fuzzy land use category wasmultiplied to the suitability value of each focal species for thesame class of land use. In this way the suitability of species in theterritory is weighted on the membership to the consideredclasses, objectively obtained on the basis of spectral reflectanceof each pixel (Lillesand and Kiefer 1999). Finally we decided tocreate a conceptual framework whose scope is to summarise theproposed methodology.

Results

In Tuscany there are 42 species of native terrestrial mammals(Agnelli 1998). The assignation of sensitivity values to eachcomponent of HIHF created the matrix shown in Table 1. Thethreshold values defined for each component of HIHF, led to thedefinition of a set of 14 focal species. The performed fuzzy landcover classification resulted in five thematic 15 maps based onmembership level to each of the five land use classes (Fig. 1). Thespectral reflectance profiles showed the expected response of eachland use class in reference to Landsat 7 ETM+bands (Lillesand andKiefer 1999). This highlighted the similarity in the average profilesof reflectance of the classes ‘‘Urban fabric’’, ‘‘Agriculture areas’’and ‘‘Shrubland’’ as a result of the ‘‘bare soil’’, especially as regardsto the near infrared (band 4). The membership values obtained bythe matrix shown in the Table 4 showed a discretecorrespondence between Boolean classes and fuzzy classes,demonstrated by the highest fuzzy membership values in thediagonal of matrix.

This is particularly true for the classes ‘‘Woodland’’, ‘‘Agricul-ture area’’ and ‘‘Wetland and water.‘‘ In some cases there werehigh membership values even among non corresponding classes(‘‘Urban Fabric’’-‘‘Agriculture area’’, ‘‘Agriculture area’’-‘‘Shrub-land’’, ‘‘Shrubland’’-‘‘Woodland’’).

The average of suitability values of the set of focal species, inrelation to considered land use classes, was redrafted by imageprocessing operations. It was carried out using a pixel-basedaverage between the habitat suitability models of each focalspecies so as to obtain a cartographic suitability model thatapplies a continuity criterion (Figs. 2A,B). The definitive model hasa resolution of 28.5 metres and a fuzzy suitability values scaleranging from 0.00 to 0.55, due to the effect of the correction withthe land cover class membership values. In order to standardise asimplified nominal scale of suitability (e.g. Boitani et al. 2002)areas with values from 0.00 to 0.18 can be considered as having nosuitability for the selected set of focal species, the values from 0.18to 0.30 can be considered having low suitability, the values from0.30 to 0.44 can be considered as having intermediate suitabilityand the values from 0.44 to 0.55 can be considered as being ofhigh suitability. The methodology used was summed up in aconceptual scheme (9 steps) and is proposed as a useful tool forplanning based on an integrated approach (Fig. 3).

Table 4Matrix to relate Boolean categories with the membership mean values to corresponding fuzzy categories.

Fuzzy membership

CLC Urban fabric Agricultural area Woodland Shrubland Wetland and water

Urban fabric 0.4890 0.3312 0.0288 0.1449 0.0060

Agricultural area 0.0298 0.6329 0.1085 0.2279 0.0010

Woodland 0.0072 0.0749 0.7973 0.1202 0.0003

Shrubland 0.0259 0.2051 0.4998 0.2677 0.0016

Wetland and water 0.0220 0.1411 0.0397 0.1504 0.6468

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Discussion

The expert-based approach proposed to select focal speciesmay facilitate the definition of a set of species sensitive to HIHFand to its components indirectly based on their ecological traits.

Using this approach it is possible to overcome, at least in apreliminary selection phase, the problem of lack of data on thesensitivity of species to HIHF as it is indirectly based on ecologicaltraits for which the level of knowledge is certainly higher. Theproposed approach may lead indirectly to assigned scores of

species-specific sensitivity to HIHF and its components; thespecies selected as the most sensitive, can be considered asindicators in the ecological network planning. The case study hasallowed, although with the limitation of having been used with asingle group (terrestrial mammals), a selection of focal specieswhich coincide with those widely known in the literature assusceptible to HIHF (see Bright 1993).

Nevertheless, in the selected set the differences between theobtained values (and thus the hierarchy of sensitivity amongspecies) should be subject to critical review by specialists,

Fig. 1. Study area and the produced fuzzy maps: A) Urban fuzzy membership; B) Agriculture fuzzy membership; C) Woodland fuzzy membership; D) Shrubland fuzzy

membership E) Wetland and water fuzzy membership.

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comparing the obtained values in an automatic way from thematrix ecological characteristics/components of HIHF with fore-ground from literature or from the original studies. Each speciesshows a different reaction to HIHF, so this model has the aim,through the characteristics of focal species, to provide indicationsto extend in a reticular way the domain of protection of themajority of the species.

The fuzzy land cover maps, as also demonstrated by otherstudies (Cayuela et al. 2006; Svoray et al. 2007; Zadnik Stirn2006), seem to better perform the real distribution of habitats infragmented landscapes. This is essential in developing suitability

models that would allow effective conservation policies. Thestatistical verification of training site chosen for the fuzzyclassification (carried out through the extraction of the averagevalues of reflectance of each class), showed how the choice ofsample sites must be as homogeneous as possible, to avoidoverlaps in spectral profiles and subsequent classificationproblems (Eastman 2006). Using satellite data at medium spatialand spectral resolution, such as Landsat data, in landscapescharacterised by high structural complexity, the separation ofdifferent components of habitat mosaic may be difficult (Lillesandand Kiefer 1999), particularly for land use classes characterised by

Fig. 2. (A) Habitat suitability model for Tuscany (central Italy). Areas with values from 0.00 to 0.18 can be considered as being unsuitable for the selected set of focal species,

the values from 0.18 to 0.30 can be considered as low suitability, the values from 0.30 to 0.44 can be considered as being of intermediate suitability and the values from 0.44

to 0.56 can be considered as having high suitability. (B). Detail of suitability model in highly complex landscape with large urban areas including residual natural patches.

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a high spatial heterogeneity, as ‘‘Agriculture areas’’, ‘‘Shrubland’’and ‘‘Urban fabric’’ (Fig. 1). The use of bands in the non-visiblespectrum (near infrared and infrared average), allowed a separa-tion of environmental categories involved in the process ofclassification. The comparison between land cover classes ex-tracted from Corine Land Cover 2000 and membership values ofthe corresponding fuzzy maps classes, strengthens the greaterreliability of the soft approach inside studies on connectivityamong habitats and HIHF of landscapes. Indeed, low membershipvalues are present in categories with high structural heterogeneityas ‘‘Shrubland’’ and ‘‘Agriculture areas’’. This suggests an inade-quacy intrinsic to Boolean methods of classification in connectionwith complex situations characterised by high structural andgeometric complexity of the territory (Foody 1996). In addition,the Boolean categories lack the capacity to describe mixed landcover categories, due to the binomial characteristic of the degreeof membership of each category (owned or non-owned) (Rocchiniand Ricotta 2007; Small 2004). Studies carried out in theMediterranean geographic areas showed how classifiers basedon fuzzy methodologies are better in situations of high spatialheterogeneity (Chirici and Corona 2006). This integrated metho-dology, taking into account the results obtained and thesubsequent analysis of comparison, appears more effective forstudies on connectivity and HIHF compared to the modelsgenerally used (based on a target species and cartographic basesobtained with a Boolean classification). It could be envisaged thatthe use of a wider range of habitat suitability values in theevaluation of land cover suitability values, related to the set ofselected focal species, and the use of higher resolution remotesensing images could better separate habitat categories duringclassification.

A subsequent analysis and compilation of results obtainedwould also identify critical areas for presence or transit of speciesand provide guidance for improved actions to offset defragmenta-tion.

We are not claiming that the utilisation of Boolean maps increating habitat suitability models should be dismissed, but attaking into account problems arising from a Boolean view ofecological entities which are being mapped should be taken intoaccount (Comber et al. 2005). Habitats are expected to gently varywithin a landscape (Fischer et al. 2004; Ingham and Samways1996), therefore creating abrupt thresholds among landscapeobjects should only create misleading information for territorymanagers and administrators (Rocchini and Ricotta 2007).

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