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International Journal of Applied Earth Observation and Geoinformation 37 (2015) 152–159

Contents lists available at ScienceDirect

International Journal of Applied Earth Observation andGeoinformation

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mportance of landscape features and Earth observation derivedabitat maps for modelling amphibian distribution in the Alta Murgiaational Park

entile Francesco Ficetolaa, Maria Adamob, Anna Bonardia, Vito De Pasqualee,ristiano Liuzzi c, Francesco Lovergineb, Francesco Marconec, Fabio Mastropasquad,ristina Tarantinob, Palma Blondab, Emilio Padoa-Schioppaa,∗

RULE – Research Unit of Landscape Ecology, Department of Earth and Environmental Sciences, University of Milano-Bicocca, ItalyNational Research Council, Institute of Intelligent Systems for Automation (CNR-ISSIA), Bari, ItalyRiserva Naturale Statale Le Cesine, Masseria Cesine, 73029 Vernole, LE, ItalyVico I Schiavelli, 9, 70020 Bitritto, BA, ItalyPlanetek S.r.L, Via Massaua, 70126 Bari, Italy

r t i c l e i n f o

rticle history:eceived 10 January 2014ccepted 9 October 2014vailable online 4 November 2014

eywords:mphibianseneral Habitat Categoryabitat mapand cover map

a b s t r a c t

Traditionally, analyses of relationships between amphibians and habitat focused on breeding environ-ments (i.e., pond features) more than on the features of the surrounding environment. Nevertheless,for most amphibians the terrestrial phase is longer than the aquatic phase, and consequently landscapefeatures (i.e., habitat mosaics) may have an important role for modelling amphibian distribution.

There were different aims in this analysis. Firstly, we compared the effectiveness of the informationprovided by land cover/use (LC/LU) classes and habitat classes defined according to a new habitat tax-onomy named General Habitat Category (GHC), which is based on the concept of biological forms ofdominant vegetation and class naturalness. The GHC map used was obtained from a pre-existing val-idated LC/LU map, by integrating spectral and spatial measurements from very high resolution Earthobservation data according to ecological expert rules involving concepts related to spatial and temporalrelationships among LC/LU and habitat classes.

Then, we investigated the importance for amphibians of the landscape surrounding ponds within theItalian Alta Murgia National Park. The work assessed whether LC/LU classes in pond surrounds are impor-tant for the presence/absence of amphibians in this area, and identified which classes are more importantfor amphibians. The results obtained can provide useful indications to management strategies aiming at

the conservation of amphibians within the study area. An information-theoretic approach was adoptedto assess whether GHC maps allow to improve the performance of species distribution models. We usedthe Akaike’s Information Criterion (AICc) to compare the effectiveness of GHC categories versus LC/LUcategories in explaining the presence/absence of pool frogs. AICc weights suggest that GHC categoriescan better explain the distribution of frogs, compared to LC/LU classes.

© 2014 Elsevier B.V. All rights reserved.

ntroduction

In the last two decades, many studies have pointed out aidespread decline of amphibians at global scale, with hundreds of

mphibian species now facing concrete extinction risk (Stuart et al.,004; Beebee and Griffiths, 2005; Ficetola et al., 2010). Diseases,lobal warming, anthropic pressures, alien species and complex

∗ Corresponding author. Tel.: +39 0264482945; fax: +39 0264482996.E-mail address: emilio.padoaschioppa@unimib.it (E. Padoa-Schioppa).

ttp://dx.doi.org/10.1016/j.jag.2014.10.003303-2434/© 2014 Elsevier B.V. All rights reserved.

interactions among previous factors are among the key causes ofsuch decline (Stuart et al., 2004; Beebee and Griffiths, 2005). Par-ticularly, habitat modification and conversion induced by harmfulhuman activities seem the most important drivers of populationdecline worldwide (Gardner et al., 2007). An increasing number ofstudies are focusing on the mechanisms influencing the patternsof species abundance and occurrence (Van Buskirk, 2005) to both

increase the understanding of the ecological processes underly-ing habitat changes (Gardner et al., 2007) and individuate the keyfactors determining variation in species distribution (Skelly et al.,1999; Dalbeck et al., 2007; Werner et al., 2007; Bonardi et al., 2011).

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G.F. Ficetola et al. / International Journal of Applied E

Traditionally, analyses of relationships between amphibians andabitat modification/conversion focused on local breeding envi-onments (i.e., pond features) alone without considering pondurrounds (Beebee, 1981, 1985) and the fact that the terrestrialhase is longer than the aquatic phase for most amphibians. In the

ast decade, studies have stressed the need and importance of anal-ses performed at the landscape (sensu Turner et al., 2001) level:errestrial habitats are necessary for the post-reproductive life ofmphibians, and can also influence the features of aquatic breed-ng wetlands (Pope et al., 2000; Van Buskirk, 2005; Ficetola et al.,009, 2011; Plaias u et al., 2012). However, our knowledge of the

mpact of landscape modification/conversion on amphibians is faro be complete, and more studies are required on the relationshipsetween amphibian distribution and habitat spatial arrangements,efore generalizations can be performed (Gardner et al., 2007).

Habitat classification is still an open problem in ecology. A vari-ty of definitions, classification taxonomies and approaches aresed in Europe, making comparison across the continent moreomplicate (Tomaselli et al., 2013; Kosmidou et al., 2014). In ordero harmonize habitat surveys a system, based on life forms ofominant vegetation and ecological concepts, has been recentlyroposed (Bunce et al., 2008). This system introduces 130 generalabitat categories (GHC) that can be defined without ambiguityuring in-field campaigns, can be translated to Annex I of Habitatsirective (HabDir, Council Directive 92/43/EEC) habitats through

key (Bunce et al., 2013) and can also be detected from spaceAdamo et al., 2014; Blonda et al., 2012; Kosmidou et al., 2014).his study was developed and tested within the European Union’seventh100 Framework Programme (EU-FP7) project Biodiversityulti-Source Monitoring System: from Space To Species (BIO SOS),

hat aimed to develop tools and models for consistent multi-annualonitoring of protected areas and their surroundings by the inte-

rated use of RS and in-field data. Earth Observation (EO) data aresed in the translation phase to GHC habitats of validated LC/LUlasses, with these obtained also from EO data classification or fromre-existing validated products such as orthophoto (by visual inter-retation) and/or in-field campaigns. BIO SOS project aimed also to

ink habitat features with species distribution.Linking GHC and species distribution is a new challenge to

mprove habitat mapping techniques. There are few studies in thatirection, in particular it has been explored the linkage betweenlants and GHC (Ortega et al., 2013). On the other side linkagesetween GHC and animal diversity still need to be developed (seelso Padoa-Schioppa et al., 2012).

The objectives of this research were: (i) obtaining a GHC maprom LC/LU categories (“GHC map”); (ii) evaluating the importancef the surrounding landscape for the presence/absence of amphib-ans in a sub-area of the whole Alta Murgia National Park (AMNP),o identify which LC/LU classes are more important for amphibians“Amphibians in the Alta Murgia (LC/LU map)”) and (iii) comparinghe effectiveness of LC/LU and GHC maps to explain the distributionf amphibians in species distribution models (“Do GHC improvehe performance of species distribution models?”). The findings ofhis paper can provide some indication for management strategiesiming at the conservation of amphibians within the study area, asnderlined in the conclusion section.

aterial and methods

tudy site and available data

The AMNP (680 km2) is located in Southern Italy, Puglia Regionnd is partly coincident within the Natura 2000 Murgia Alta siteSCI/SPA IT9120007, according to Habitats Directive 92/43 and Birdirective 147/2009), (see Fig. 1). It consists of a calcareous upland

bservation and Geoinformation 37 (2015) 152–159 153

with a typical dry karst landscape. The sub natural vegetation typemostly represented are Mediterranean steppe grasslands. Thosegrasslands cover almost 24% of the total area of the site, represent-ing one of the most important areas for the conservation of thisecosystem type in Europe. Substantial losses have occurred sincethe beginning of the 20th century (Boccaccio et al., 2013). As withmost grasslands of Mediterranean and Western Palaearctic regions,these semi-natural communities have developed through a mix ofanthropogenic and natural processes over long periods of grazingby domestic stock, cutting and deliberate light burning regimes,and are maintained by human activities, mainly through grazing(Suarez, 1994; Turbé et al., 2010). Semi-natural calcareous grass-lands are among the most species-rich plant communities in Europe(Wilson et al., 2012) and they host a remarkable set of endemic andprotected species. Agricultural intensification and land abandon-ment together provide major pressures on biodiversity. See Ficetolaet al. (2014) for additional details.

A pre-existing validated LC/LU map (scale 1:5000, dated 2006)provided by Puglia Region was used for producing the GHC map ina sub-area covered by EO images (see below). The original CORINEtaxonomy was converted to the Food and Agricultural Organisation(FAO) Land Cover Classification System (LCCS) taxonomy whichwas found to be the most useful for translating EO-derived LC/LU tohabitat categories (Tomaselli et al., 2013). Fig. 2a shows the LC/LUmap of the study area, which includes 20 LCCS classes belongingto the following LCCS categories: bare areas (B16); artificial sur-faces (B15), including roads and urban areas; cultivated terrestrialvegetated areas (A11), including trees broadleaved evergreen (suchas olive groves, A1.A7.A9) and deciduous (A1.A7.A10); natural andsemi-natural terrestrial vegetation (A12), including woody trees(A1.A3) and woody shrubs (A1.A4), herbaceous forbs (A2.A5) andgraminoids (A2.A6); inland water (B28).

Two multi-temporal WorldView-2 VHR images, dated April2011 and October 2011, were available for a sub-area of the park(about 500 km2). Both images were ortho-rectified and calibratedin TOA (Top of Atmosphere) reflectance values (Fig. 2b and c). Thespatial resolution (2 m) was compatible with the land cover mapscale. The spectral indices extracted from the images were inte-grated with spatial and temporal relations to disambiguate theone-to-many LCCS to GHC relations (see “Do GHC improve the per-formance of species distribution models?”). Due to differences inthe dates of the pre-existing LC/LU map and EO image acquisitions,only reference samples corresponding to no changed classes wereselected through visual image inspection for validating the GHCproducts. As well-known, VHR images are tasked on-demand, sofor this site no archive data were found corresponding to the yearof the pre-existing LC/LU map production.

LC/LU to GHC class translation

In the context of the BIO SOS project, a framework was devel-oped to automatically translate LC/LU classes to GHC categories(Kosmidou et al., 2014; Adamo et al., 2014). Due to the discrep-ancies between class definitions in the two different domains, asystematic translation was proven challenging as several one-to-many and many-to-many LC/LU to GHC class relations (Kosmidouet al., 2014) were found and expert knowledge was used to pro-vide GHC maps for ponds (Adamo et al., 2014). In this paper, thesame expert knowledge rules are applied to describe LC/LU to GHCtranslation in Murgia Alta area, demonstrating their effectivenessand generalisation to a different landscape. The rules are briefly

reported hereafter:

(1) Spatial topological relations (e.g., topological adjacency, enclo-sure) are considered between objects. In particular, the

154 G.F. Ficetola et al. / International Journal of Applied Earth Observation and Geoinformation 37 (2015) 152–159

F uglia

m onal Pb

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ig. 1. Location of Puglia region in Italy (a), the Alta Murgia National Park within Papping (c). The distribution of the amphibian breeding localities within the Nati

uffer to the boundary of the National Park (dotted line).

adjacency relation to urban categories was used to evaluate if avegetated object can be considered part of the urban context;

2) Temporal relations (i.e., plant phenology) are used to deal withthe phenological properties of vegetation and discriminate sim-ilar spectral classes. At least two images had to be introduced:the ones corresponding to the peak of vegetation productiv-ity (April/May; Fig. 2b) and to the post-flush period (October,Fig. 2c);

3) Geometric attributes (i.e., regularity of objects boundaries). Therequirement of regularity in the shape of a segment was used.Based on a rectangular approximation, this feature describeshow jagged a segment is. The smallest rectangle enclosing theimage object is created and the Border Index is calculated asthe ratio between the perimeter length of the object and thesmallest enclosing rectangle (Trimble, 2011).

4) Contextual information. First-order entropy extracted by theimage corresponding to the peak of biomass has been consid-ered as a proxy of plant height information identifying twodifferent height clusters (Adamo et al., 2014; Petrou et al., 2012).

5) Prior spectral knowledge on class spectral signature. Two spec-tral indices, the Water Index (WI; Morris and Dupigny-Giroux,2010) and the Greenness Index (GI; Ritchie et al., 2010) wereused as their bands ratios are known (reference) to discriminatewell between vegetated and water covered pixels respectively.These are defined as:

I = �(�BLUE)�(�NIR)

(1)

�(�RED)

I =

�(�NIR)(2)

here � is the TOA value in the bands considered and is calculateds the mean value inside the object.

(b) and the study area (approx. 500 km2) for which EO data was available for GHCark and its surroundings is also shown (circles). The black line represents a 2-km

Species distribution surveys

Amphibian distribution in the AMNP was assessed by repeatedsurveys carried out in 2010–2011, during the amphibian breedingseason, in over 63 ponds and their surrounds. Potentially suit-able ponds were identified on the basis of bibliographic (scientificand grey literature) information and direct field surveys. Multipletechniques (i.e., visual encounter surveys, dip netting, audio pointcounts) were used to ascertain species’ presence/absence. Addi-tional details on the study area and sampling methods can be foundin Marcone et al. (2012). A buffer area of 500 m radius was con-sidered around each pond and the percentage of each LC/LU classfound in this buffer area (Gustafson et al., 2011) was measured withArcGIS® Desktop 10 tools. Amphibian nomenclature followed thechecklist of the European herpetological atlas (Sillero et al., 2014).

Species distribution modelling and statistical analyses

We used an information theoretic approach, based on Akaike’sInformation Criterion corrected for small sample size (AICc)(Burnham and Anderson, 2002), to identify the combination ofvariables best describing the distribution (presence/absence) ofamphibians detected in >10 sites. As dependent variables, we usedthe percentage land cover for each LC/LU category within the500 m buffer. We built generalised linear models (GLM) assum-ing binomial error, including all possible combinations of variables,and used them as species distribution models (SDMs). If pres-ence/absence data are available, methods using presence/absenceoutperform presence/background or presence-only methods (Liet al., 2011). For each model, we calculated AICc and �-AICc, which

is the difference in AICc between a candidate and the model withlowest AICc (i.e., the best model). The use of AICc as sole selec-tion criterion may select overly complex models. Therefore, as anadditional criterion, we considered a complex model only if it had

G.F. Ficetola et al. / International Journal of Applied Earth Observation and Geoinformation 37 (2015) 152–159 155

Fig. 2. (a) Pre-existing land use map for Apulia Region: the class legend uses the FAO-LCCS taxonomy (see Table 4 for more explanations). Grassland, in cyan, is coded asA12/A2.A6.E6; (b) Worldview-2 mosaic of images acquired on 19th April 2011, 2 m spatial resolution, radiometrically calibrated into TOA values, depicted in false colours (R:band 5-Red, G: band 7-Nir1, B: band 2-Blue). Default image histogram stretching: ENVI linear stretching 2%. The image is representative of the vegetation Peak of Biomass(PoB). (c) Worldview-2 image acquired on 5th October 2011, 2 m spatial resolution, radiometrically calibrated into TOA values, depicted in false colours (R: band 5-Red, G:band 7-Nir1, B: band 2-Blue). Default image histogram stretching: ENVI linear stretching 2%. The image is representative of the vegetation Post Peak of Biomass (PostPoB).( rred to

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fu

For interpretation of the references to color in this figure legend, the reader is refe

�-AIC lower than the �-AIC of all its simpler nested modelsRichards et al., 2011). Per each model i, we calculated the AICeight wi, which is the probability for a model to be the best one

mong the candidates (Richards et al., 2011). Models were com-ared among them and with the null model (i.e., the model non

ncluding any of the land use variables). AICc values lower thanhe AICc of the null model indicate that landscape variables areikely to be important predictors of amphibian distribution. Welso used a likelihood ratio test to test the significance of variables.e performed analyses using the software R (R Development Core

eam, 2012); we extracted and compared the AICc values using theackage MuMIn (Barton, 2011).

We used also an information-theoretic approach to assesshether the GHC maps allow to improve the performance of SDMs.

irst, we used the procedure detailed above to identify the best AICcodel relating the presence/absence of pool frogs to: (1) GHC cat-

gories; (2) LC/LU categories obtained from the available map. Theest models identified through the two sets of land cover were then

ompared as explained before, using AICc.

The comparison between GHC and LC/LU categories was per-ormed on the sub-part of the area covered by available EO dataseful for providing the GHC map (Fig. 1c). Therefore, within this

the web version of this article.)

area the analysis was focused on the pool frogs (Pelophylax kl. escu-lentus/lessonae), which was the species with more presence data inthe area.

Results and discussion

GHC map

The LC/LU and GHC classes are listed in Tables 4 and 5. TheGHC map obtained from the available land cover map accordingto the LC/LU to GHC mapping schemes described in (Kosmidouet al., 2014) is shown in Fig. 3. One-to-many and many-to-manymapping ambiguities between the two class domains are solvedusing EO images and the expert rules discussed in "LC/LU to GHCclass translation". In the Alta Murgia site, still some ambiguities inthe mapping of some LC classes to different GHC classes remain,such as those related to the association of natural woody plantsto different TRS (natural trees or shrubs) subcategories, such as

TRS/LPH (natural trees or shrubs/low phanerophytes), TRS/MPH(trees or shrubs/mid phanerophytes), TRS/TPH (trees or shrubs/tallphanerophyte) and TRS/FPH (trees or shrubs/tall phanerophyte).However, such ambiguities can be solved as soon as LIDAR data for

156 G.F. Ficetola et al. / International Journal of Applied Earth Observation and Geoinformation 37 (2015) 152–159

ed for

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Fig. 3. GHC map produc

lant height information extraction will be collected for the site, aslready done for other protected areas (Adamo et al., 2014).

mphibians in the Alta Murgia (LC/LU map)

Five amphibians were detected in the 63 ponds: the Italian newtissotriton italicus (occurrence = 13 sites); the Italian crested newtriturus carnifex (2 sites); the green toad Bufo viridis (14 sites); theommon toad Bufo bufo (9 sites); the pool frogs Pelophylax kl. escu-entus/lessonae (32 sites). Therefore, analyses were performed on

hree species: Italian newt, green toad and pool frogs.

Average richness was 1.1 species per pond. In 19 ponds, morehan 1 amphibian species was detected (maximum: 4 species, inwo ponds). Amphibians were present through the whole park.

Murgia Alta study area.

For all the study species analysed, LC/LU classes were signifi-cantly related to the presence/absence in ponds (Tables 1 and 2).Furthermore, for all study species the models including land covertypes showed AICc values much lower than the null models. Actu-ally, for all species the AICc weight of the null model was 0.012or lower (Table 1), confirming that the surrounding landscapeis extremely important for the distribution of aquatic breedingamphibians: on the basis of AIC weights, the probability that thelandscape is not important is 1% or less (Lukacs et al., 2007).

For the Italian newt, three variables were included in the bestmodel: cover of shrublands, vineyards and woody cultures (Nagelk-

erke’s R2 = 0.35). The newt was associated with ponds surroundedby shrubby landscapes, and with limited cover by woody cultures(mostly orchards in the study area) or vineyards, although the rela-tionship with vineyards was not significant (Tables 1 and 2). All

G.F. Ficetola et al. / International Journal of Applied Earth Observation and Geoinformation 37 (2015) 152–159 157

Table 1Candidate generalised linear models for amphibians. K: number of parameters included in the model; AICc: Akaike’s information Criterion, corrected for small sample size;�AICc: difference in AICc values with the best model; w: AICc weight of the model.

Species Model rank Variables K AICc �AICc w

Lissotriton italicus (Italian newt) 1 Shrublands, vineyard, woody cultures 4 56.69 0.000 0.4532 Shrublands, woody cultures 3 57.11 0.417 0.3683 Vineyard, woody cultures 3 60.06 3.374 0.0844 Woody cultures 2 60.27 3.583 0.0765 Vineyards 2 63.36 6.671 0.0166 Null model 1 66.21 9.520 0.004

Bufo viridis (Green toad) 1 Herbaceous cultures, vineyards 3 60.25 0.536 0.8382 Vineyards 2 64.36 4.644 0.1073 Herbaceous cultures 2 66.18 6.463 0.0434 Null model 1 68.81 9.090 0.012

Pelophylax kl. esculentus/lessonae (Pool frog) 1 Crops 2 80.61 0 0.9062 Grassland, olive groves 3 87.83 7.228 0.0243 Grassland, woody cultures 3 88.41 7.804 0.0184 Grassland, vineyards 3 88.79 8.186 0.015

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was very limited (Table 1). The best model indicated a strong, pos-itive relationship with crops (Nagelkerke’s R2 = 0.22). Pool frogsare termophilous, and prefer sunny wetlands (Ficetola and DeBernardi, 2004). Therefore small waterbodies associated to open

Table 4List of LC classes in LCCS taxonomy in the study area.

LCCS code LCCS description

B16/A1.A3.A7 Bare Areas/Consolidated.Bare RockB15/A1.A3.A7.A8 Artificial Surfaces/Builtup.Linear.Roads.PavedB15/A1.A4.A12.A17 Artificial Surfaces/Builtup.Non-

Linear.IndustrialAreas.ScatteredDensity

5 Woody

6 Olive gr7 Null mo

he other candidate models included the same variables (Table 1),onfirming the robustness of the best model. As other speciesf small-bodied newts, it is not associated to forested areas, stillt takes advantage of relatively natural covers within landscape,ike scrubs (Denoel and Ficetola, 2007; Denoël and Ficetola, 2008;enoël and Lehmann 2006). The dependency of this species from

hrublands may make it complex the conservation of this species,s shrublands often undergo encroachment, and ecological succes-ion often transforms them into forests. Actually, populations ofhis newt are declining across the species’ range (Arntzen et al.,009), indicating that actions are needed for the conservation ofhis species in the long term: the preservation of landscape featureseeded for its terrestrial activity will certainly be an important step.

Conversely, the green toad was positively associated with vine-ards and herbaceous crops (Table 2) (Nagelkerke’s R2 = 0.28). Thisoad is an opportunistic amphibian that can take advantage by themall water reservoirs associated to traditional agricultural activi-ies. In this case, species conservation would require very differenttrategies, compared to the conservation of the Italian newt. For

reen toad, the preservation in the park would be favoured by theaintenance of traditional agriculture, of the small elements where

he toad can find shelter (e.g., stone walls), and ideally by small,

able 2ariables included in the best models for the three species included into analyses.

Species Estimate SE �2 d.f. P

Lissotriton italicus (Italian newt)Vineyards −20.144 29.908 2.700 1 0.100Woody cultures −425.460 33653.273 10.980 1 0.001Shrubland 22.471 13.688 5.657 1 0.017

Bufo viridis (Green toad)Vineyards 4.304 1.609 8.134 1 0.004Herbaceous cultures 27.183 14.866 6.315 1 0.012

Pelophylax kl. esculentus/lessonae (Pool frog)Crops 2.050 0.677 10.915 1 0.001

able 3omparison of best models including at least one land use variable, obtained usingHC and LC/LU maps. The best model is in bold. K: number of parameters included

n the model; AICc: Akaike’s information criterion, corrected for small sample size;AICc: difference in AICc values with the best model; w: AICc weight of the model.

Source Variables K AICc �AICc w

GHC Woody coltures 2 30.73 0.000 0.597LC/LU Crops 2 31.51 0.782 0.403

es 2 89.15 8.546 0.0132 89.22 8.616 0.0121 89.39 8.781 0.011

temporary ponds in sunny areas (Bologna and Giacoma, 2006). Alsofor the green toad, the best model was quite robust, as all the othercandidate models included a similar set of variables.

Finally, the situation for pool frogs was more complex. Poolfrogs are among the more aquatic amphibians. Nevertheless, alsofor this frog landscape features were extremely important, as thesupport of the null model, not including any landscape variable,

B15/A1.A4.A13.A16 ArtificialSurfaces/Builtup.Non-Linear.UrbanAreas.LowDensity

B15/A1.A4.A13.A17 Artificial Surfaces/Builtup.Non-Linear.UrbanAreas.ScatteredDensity

B28/A1.A5 Natural WaterBodies/Water.StandingA11/A1.A7.A10.W8 Cultivated and Managed Terrestrial

Areas/Trees.Broadleaved.Deciduous.OrchardsA11/A1.A7.A9.W8 Cultivated and Managed Terrestrial

Areas/Trees.Broadleaved.Evergreen.OrchardsA11/A1.A8.A9.W7 Cultivated and Managed Terrestrial

Areas/Trees.Needleleaved.Evergreen.PlantationsA11/A2.A7.A10.W8 Cultivated and Managed Terrestrial

Areas/Shrubs.Broadleaved.Deciduous.OrchardsA11.A3.A4 Cultivated and Managed Terrestrial

Areas/Herbaceous.GraminoidsA11.A3.A5 Cultivated and Managed Terrestrial

Areas/Herbaceous.Non-GraminoidsA12/A1.A3.D1.E2 Natural Terrestrial

Vegetation/Woody.Trees.Broadleaved.DeciduousA12/A1.A4.D1.E1 Natural Terrestrial

Vegetation/Woody.Shrubs.Broadleaved.EvergreenA12/A1.A4.D1.E2 Natural Terrestrial

Vegetation/Woody.Shrubs.Broadleaved.DeciduousA12/A2 Natural Terrestrial Vegetation/HerbaceousA12/A2.A5.E6 Natural Terrestrial

Vegetation/Herbaceous.Forbs.PerennialA12/A2.A5.E7 Natural Terrestrial

Vegetation/Herbaceous.Forbs.AnnualA12/A2.A6 Natural Terrestrial

Vegetation/Herbaceous.GraminoidsA12/A2.A6.E6 Natural Terrestrial

Vegetation/Herbaceous.Graminoids.Perennial

158 G.F. Ficetola et al. / International Journal of Applied Earth O

Table 5List of General Habitat Categories (GHC) in the study area.

GHC supercategory/primarycode acronyms

GHC supercategory/primary codevernacular name

CUL(WOC) Cultivated/woody cropsCUL(CRO) Cultivated/herbaceous cropsHER(CHE) Herbaceous/caespitose hemicryptophytesHER(THE) Herbaceous/thaerophytesHER(LHE) OR HER(HCH) ORHER(GEO)

Herbaceous/leafy hemicryptophytes ORHerbaceous/chamaephyte ORherbaceous/geophytes

HER(THE) OR HER(GEO) Herbaceous/thaerophytes ORherbaceous/geophytes

SPV(AQU) Sparsely vegetated/aquaticSPV(ROC) Sparsely vegetated/rockURB(NON) Urban/non vegetatedURB(TRE) Urban/woody vegetationURB(GRA) Urban/herbaceousURB(ART) Urban/artificialURB(ART/ROA) Urban/artificial/road pavedURB(VEG) OR URB(GRA) Urban/crops OR Urban/herbaceousURB(TRE) OR URB(VEG) Urban/woody vegetation OR Urban/cropsTRS(DCH/DEC) ORTRS(SCH/DEC) ORTRS(LPH/DEC) ORTRS(MPH/DEC)

Trees or shrubs/dwarfchamaephytes/deciduous ORTrees or shrubs/shrubbychamaephytes/deciduous ORTrees or shrubs/lowphanerophytes/deciduous ORTrees or shrubs/midphanerophytes/deciduous

TRS(DCH/EVR) ORTRS(SCH/EVR) ORTRS(LPH/EVR) ORTRS(MPH/EVR)

Trees or shrubs/dwarfchamaephytes/evergreen ORTrees or shrubs/shrubbychamaephytes/evergreen ORTrees or shrubs/lowphanerophytes/evergreen ORTrees or shrubs/midphanerophytes/evergreen

TRS(TPH/EVR) Trees or shrubs/tallphanerophytes/evergreen

TRS(TPH/EVR/CON) ORTRS(FPH/EVR/CON)

Trees or shrubs/tallphanerophytes/evergreen/coniferous ORTrees or shrubs/forestphanerophytes/evergreen/coniferous

TRS(TPH/DEC) Trees or shrubs/tallphanerophytes/deciduous

TRS(TPH/DEC) ORTRS(FPH/DEC) ORTRS(GPH/DEC)

Trees or shrubs/tallphanerophytes/deciduous ORTrees or shrubs/forestphanerophytes/deciduous ORTrees or shrubs/mega forest

atis

D

a(

rtfmpAmf

servation biology? Biol. Conserv. 125, 271–285.

phanerophytes/deciduous

gricultural fields are often occupied by this species. Pool frogs arehe most common amphibians within the park, and this can be eas-ly explained by their ecological plasticity, and by their ability tourvive in areas exploited by human activities.

o GHC improve the performance of species distribution models?

Twenty-one amphibian breeding ponds were located within therea covered by GHC. Pool frogs were present in 9 out of these ponds43%).

For the GHC maps, the best-AIC model suggested a positiveelationship between pool frogs and the GHC category “woody cul-ures”. This model was similar to models 2–5 built for pool frogsor the whole park (see Table 1, pool frog models). For the LC/LU

ap, the best-AIC model for this subset of ponds confirmed theositive relationship with crops (Tables 1 and 3). The difference in

ICc-value between the model for the entire park and the subsetay be explained by the lower number of ponds occupied by pool

rogs.

bservation and Geoinformation 37 (2015) 152–159

The best GHC model showed an AICc value lower than the bestLC/LU model (Table 3). The difference between the two models waslimited (0.78 AICc units). Still, on the basis of AICc weight, the sup-port of the GHC model was 1.4 times higher than the support of theLC/LU model. The limited differences between the models probablyarise because of the small number of ponds falling within the areacurrently covered by GHCs. Actually, the number of occupied pondswas below the threshold of 10 occupied sites used in the analysisof the whole park.

Conclusions

This analysis suggests that landscape features, obtained byLC/LU maps, are extremely important to explain the distributionof amphibians. All study species breed in water, and two of them(Italian newt and pool frog) require water for long periods. Theimportance of landscape variables for these species is thereforestriking, and confirms the necessity to perform management overbroad spatial scales. In other words, the conservation of breedingponds is not enough for these semiaquatic species, and wide areaswith suitable landscape (e.g., shrublands for the newt, traditionalagriculture for the green toad) need to be maintained (Gibbons,2003; Ficetola et al., 2009, 2011).

The presented analyses used LC/LU and GHC maps obtainedthrough both traditional and new technologies which are basedon the integration of EO and in situ data. Such technologies allowto obtain maps better representing the amphibian terrestrial habi-tats. This will probably permit to better identify the landscaperequirements for amphibians, and therefore obtain managementindications that allow to better target the need of these threatenedvertebrates.

Our results suggest that GHC map provides a better descrip-tion of the terrestrial habitat, compared with the LC/LU map. Theexpansion of the area covered by GHC will certainly allow moreaccurate comparisons of the models performance and to obtain bet-ter models describing species distribution that can be used for themanagement of the park.

Acknowledgements

We wish to thank two anonymous Reviewers for theirvaluable suggestions. This work was supported by the Euro-pean Community’s Seventh Framework Programme, within theFP7/SPA.2010.1.1-04: “Stimulating the development of GMES ser-vices in specific area”, under grant agreement 263435 for theproject Biodiversity Multi-Source Monitoring System: From SpaceTo Species. (BIO SOS), www.biosos.eu. GFF was funded by a fellow-ship by University of Milano-Bicocca and AB was funded by the EU7FP BIO SOS project.

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