Landscape Research
Habitat Suitability and Landscape
Structure: A Maximum Entropy
Approach in a Mediterranean Area
a
b
a
c
Valerio Amici , Britta Eggers , Francesco Geri & Corrado Battisti
a
BIOCONNET, Biodiversity and Conservation Network, Department
of Life Sciences, University of Siena, Italy.
b
Thuenen Institute for World Forestry, Hamburg, Germany.
c
Environmental Service, Province of Rome, Italy.
Published online: 09 May 2013.
To cite this article: Valerio Amici, Britta Eggers, Francesco Geri & Corrado Battisti (2015) Habitat
Suitability and Landscape Structure: A Maximum Entropy Approach in a Mediterranean Area,
Landscape Research, 40:2, 208-225,
Landscape Research, 2015
Vol. 20, No. 2, 208–225, http://dx.doi.org/10.1080/01426397.2013.774329
Habitat Suitability and Landscape Structure:
A Maximum Entropy Approach in a
Mediterranean Area
VALERIO AMICI*, BRITTA EGGERS**, FRANCESCO GERI* &
CORRADO BATTISTI***
*
BIOCONNET, Biodiversity and Conservation Network, Department of Life Sciences, University of Siena,
Italy **Thuenen Institute for World Forestry, Hamburg, Germany ***Environmental Service, Province of
Rome, Italy
ABSTRACT Species distribution models have recently become important tools in ecological
research. Prediction of suitable habitats for threatened and endangered species is essential for
the conservation and management of their native habitats. A landscape scale approach is relevant
for biodiversity conservation since landscape planning and management are generally conducted
at wide spatial scales, focusing on areas with complex landscape configuration as a consequence
of human activities. The aims of this study were to test a maximum entropy approach (Maxent) to
the development of a niche-based model for species of conservation interest and to relate this
model to landscape structure metrics. The results obtained here showed a good predictive power
of Maxent for the three target species and highlighted the importance of landscape structure
analysis for the detection of patterns of habitat suitability. Moreover, this work stressed that
combining classical environmental information with landscape structure in analysing habitat
suitability for species of conservation interest may be used to guide conservation efforts and
landscape management practices.
KEY WORDS: ecological niche, habitat suitability, landscape metrics, Maxent, species distribution
model
Introduction
Species distribution models have recently become increasingly important tools for analysing species–habitat relationships in ecological and conservation research (Cayuela
et al., 2009; Guisan & Thuiller, 2005). Predicting suitable potential habitats, by relating
field observations to environmental variables, is essential for the conservation of
threatened and endangered species (Gaston, 1996).
Over the last two decades there have been many developments in the field of species
distribution modelling (Elith & Leathwick, 2009). A major distinction among distribution models is the required data input and the distribution and abundance of this data
Correspondence Address: Valerio Amici, BIOCONNET, Biodiversity and Conservation Network, Department
of Life Sciences, University of Siena, Via P. A. Mattioli 4, 53100 Siena, Italy. Email: valerio.amici@gmail.
com
Ó 2013 Landscape Research Group Ltd
Habitat Suitability and Landscape Structure 209
over the study area. In most regions, systematic biological survey data on threatened
and endangered species are occasional, with a limited geographical coverage and clustered, making the most common modelling approaches difficult (Engler et al., 2004;
Ferrier et al., 2002).
The principal task of habitat models is to predict habitat suitability for species as a
function of the given environmental variables (Basille et al., 2008). We define ‘habitat
suitability’ as the ability of a landscape unit (a pixel or polygon) to support survival
and reproduction of a species (Amici et al., 2010). Habitat suitability models are based
on functional relationships between individual species and habitat variables on a variable suitability index scale (Larson et al., 2003). Habitat suitability index scores are
usually calculated using a mathematical formula representing hypothesised relationships
among the individual suitability indices.
Although it is widely recognised that species occupancy patterns reflect their ecological
traits, the relationship between suitable habitats and sites actually occupied is not strictly
deterministic. Indeed both local history (e.g. disturbances) and contemporary metapopulation dynamics may significantly affect the patterns of species occupancy. As a
consequence individuals of a target species may occur in unsuitable habitats as belonging
to sink populations and vice versa, individuals of the same species may not occur in
potentially suitable habitats due to historical and recent disturbances.
Habitat suitability modelling techniques take either a deductive or inductive approach:
the deductive method (also known as deterministic method) uses spatial data and specific
ecological and biological knowledge of each entity analysed to determine the corresponding ecological requirements (Guisan & Zimmermann, 2000). The inductive method
(also known as probabilistic method) does not use suitability attributions but contextbased observations in the field to determine the optimal intervals of environmental
parameters, corresponding to the positive presence of the species (Glenz et al., 2001).
The inductive method is the most commonly utilised, especially in the niche-based
distribution model (Guisan & Zimmermann, 2000).
A niche-based model represents an approximation of a species’ ecological niche in the
examined environmental dimensions (Anderson et al., 2002). A species’ fundamental
niche consists of the set of all conditions that allow for its long-term survival, whereas
its realised niche is that subset of the fundamental niche that the species actually occupies (Holt, 2009; Hutchinson, 1957; Levin, 2009; Soberón & Nakamura, 2009). The data
available to realise niche-based models typically consist of: i) a set of geographic coordinates where the species have been observed, and ii) data on a number of environmental
variables, such as average temperature, average rainfall, elevation, etc. The environmental variables selected to build models are generally contingent upon the target species
and the hypotheses being investigated. Subsequently, the principal purpose of species
distribution models is to predict the potential distribution of the areas that satisfy the
requirements of the species’ ecological niche (Anderson & Martínez-Meyer, 2004). The
potential distribution describes where conditions are suitable for the long-term species
survival, and is thus of great importance for conservation.
With the advent of the landscape paradigm in ecology, there has been great attention
paid to how the landscape configuration or structure, which is determined by its type of
use and by the size, shape, arrangement and distribution of individual landscape
elements, affects species distribution and population dynamics (Turner et al., 2001).
However, the impacts of the landscape configuration on habitat suitability are often
210 V. Amici et al.
difficult to disentangle, and this has major implications to habitat loss prevention and/or
habitat restoration (Mortelliti et al., 2010, 2011).
In the Mediterranean region, landscape configuration acquires major importance due
to its great habitat heterogeneity, attributable both to topographical and climatic variability and to historical and recent human influence (Cowling et al., 1992; Tews et al.,
2004). This results in highly heterogeneous, fine-grain landscapes, in which a large
number of patches of different land use and natural vegetation coexist (Burel & Baudry,
1995; Farina, 1997; see the ‘arlequin landscapes’ in Blondel & Aronson, 1999). Several
species developed specific responses to this complexity, modifying their patterns of
occupancy; this age-old adaptation both to environmental conditions and to landscape
transformation, fragmentation and degradation has often been associated in the Mediterranean with greater biodiversity (Atauri & de Lucio, 2001; González Bernáldez, 1992;
Naveh, 1994; Ruiz, 1990).
The purpose of this study was to develop niche-based models for species of conservation interest in a complex Mediterranean landscape using a comprehensive suite of
environmental variables.
Specific objectives were: i) test a maximum entropy (Maxent) approach to estimate
the probability distribution of three target species selected following an opportunistic
approach, and ii) evaluate the correlation between landscape structure patterns and
habitat suitability as estimated through the Maxent model.
Material and Methods
Study Area
The study area is the entire territory of Tuscany (Italy, 42°–44° North latitude,
9°–12° East longitude, WGS84; Figure 1), a Mediterranean region having an area of
about 19 720 km2 of which 44% is covered by forests, while the agriculture areas
cover about 46% (APAT, 2005). Forests vary from the evergreen Mediterranean forests dominated by Quercus ilex, along the coastlines, to the Fagus sylvatica and
Abies alba forests of mountain sites. The agriculture types that take up the larger
surface area include intensive non-irrigated arable lands alternated with traditional
agro-ecosystems. The topography varies from the plain areas near the coastline and
around the principal river valleys to the hilly and mountainous zones towards the
Apennine chain. By orographic point of view, approximately two-thirds of the region
is covered by hilly areas, one-fifth by mountains and only one-tenth by plains and
valleys. The climate ranges from typically Mediterranean to temperate cold following
the altitudinal and latitudinal gradients and the distance from the sea (Rapetti &
Vittorini, 1995).
Modelling Procedure
In this study, we focused on terrestrial mammals because they constitute a group of
high interest to study landscape-scale processes in a complex landscape (Amici & Battisti, 2009; Bright, 1993).
We selected from the Tuscany Natural Repertorie (Re.Na.To.), three target species on
the basis of their widespread distribution of sites across the region, their conservation
Habitat Suitability and Landscape Structure 211
Figure 1. Study area.
interest and their sensitivity to landscape fragmentation: Muscardinus avellanarius (common dormouse; Linnaeus, 1758), Martes martes (European pine marten; Linnaeus,
1758) and Mustela putorius (European polecat; Linnaeus, 1758). Studies suggest that the
three selected species are vulnerable to habitat fragmentation and loss of suitable habitats
(Bailey et al., 2002; Bright, 1993; Hargis et al., 1999; Møller et al., 2004; Spinozzi
et al., 2012; Virgós & García, 2002). Moreover, due to some of their ecological traits
(large area requirements, medium–high trophic level and high ecological specialisation),
these species may be considered umbrella for a large number of other forest and mosaic
species (Amici & Battisti, 2009; Biondi et al., 2003; Santolini et al., 2000). Re.Na.To. is
a natural repertoire founded by Tuscany Region, obtained by collecting data on terrestrial
fauna, flora and vegetation in the Tuscan territory (Sposimo & Castelli, 2005). The
essential information in Re.Na.To is the reporting, where this term refers to the presence
of data on a given species (or habitat or plant communities) taken from a particular
source data (e.g. field sampling, publication) in a certain location at a certain date (Sposimo & Castelli, 2005). Data used in this research, extracted from Re.Na.To repertoire, are
30 records for each target species.
We considered 20 environmental variables as potential predictors of our target species’ habitat distribution, grouped into following features: land use, topography, road
distance, distance from water bodies and climate.
As for the land use variables, we performed a fuzzy classification of an ortho-Landsat
ETM+ image (path 192, row 030, acquisition date 20 June 2000; spatial resolution
30 meters) covering the whole Tuscan region. The bands used were: band 1 (blue,
0.45–0.515 μm), band 2 (green, 0.525–0.605 μm), band 3 (red, 0.63–0.69 μm), band 4
212 V. Amici et al.
(near infrared, 0.75–9.90 μm), band 5 (middle infrared, 1.55–1.75 μm) and band 7
(middle infrared, 2.09–2.35 μm). Band 6 was not considered due to the much larger
pixel size than the other bands (60 meters of ground resolution opposed to 30 meters of
the other bands). In order to perform land use fuzzy classification, we applied a supervised classification approach by selecting known training sites, five for each land cover
class, based on field data belonging to seven land cover classes (artificial areas, cropland and pastures, forests, grassland, moors and heathlands, shrublands and maquis,
water bodies).
In order to obtain topographic variables (altitude, slope, aspect and solar radiation)
we achieved a 10 meters digital elevation model of the Tuscany region, (DEM) re-sampled by a nearest neighbour algorithm at a spatial resolution of 30 m and processed
with an algorithm of terrain analysis with the production of derivate images.
The ‘Road distance’ and ‘Distance from water bodies’ variables were obtained
through a distance image processing operator from vector features representing the
transport network and the hydrographic network of the Tuscan region.
The climatic variables (1990–2000) were obtained by spatial interpolation (inverse
distance weighting method) of 130 climatic stations operated by the Regional Agency
for Innovation in Agriculture (ARSIA). All the image processing operations were performed using Grass GIS and QGIS software.
A correlation analysis was performed, using the software R (R Development Core
Team, 2010), in order to exclude pairs of related variables from the model.
We used a modelling method called Maximum Entropy distribution or Maxent (Elith
et al., 2011; Phillips & Dudík, 2008; Phillips et al., 2006) which has been found to perform best among many different modelling methods (Elith et al., 2006; Ortega-Huerta
& Peterson, 2008; Wisz et al., 2008), and may remain effective despite small sample
sizes (Benito et al., 2009; Hernandez et al., 2006; Papes & Gaubert, 2007; Pearson
et al., 2007; Wisz et al., 2008). The reliability of Maxent has been confirmed, for
example, by its capacity to predict the outcome of introductions of invasive species outside the native range (Ficetola et al., 2007) and novel presence localities for poorly
known species (Pearson et al., 2007). It has been also shown that the Maxent models
can be easily interpreted by practitioners (Philips et al., 2006), a property of great
practical importance in developing suitability models that would allow effective
conservation policies.
Maxent is a maximum entropy based machine learning program method that applies
the maximum entropy principle to estimate the distribution of a species (http://www.cs.
princeton.edu/~schapire/maxent/; last accessed 29 September 2010). Entropy is a fundamental concept in information theory: it is the measure of the amount of information
that is lost when the value of a random variable is not known (Shannon, 1948). In
Maxent modelling the entropy measures the lack of information on the characteristics
of a physical system: the larger the information, the smaller the entropy (Phillips et al.,
2006) The entropy measured on a grid cell containing an occurrence record of a known
species is expected to be low, whereas the entropy measured on a grid cell on which
we do not know all the ecological constraints is expected to be high (Phillips & Dudík,
2008). Thus, Maxent evaluates the suitability of each grid cell as a function of environmental variables (Ficetola et al., 2010; Kumar & Stohlgren, 2009). The output of Maxent is a suitability map varying from 0 (no suitability) to 1 (maximum suitability).
Habitat Suitability and Landscape Structure 213
In this research the sampled presence records and the environmental variables were
used to model potential distribution of the three target species.
In order to assess the predictive performance of the model, we followed the most
commonly used approach that involves the use of the Receiver Operating Characteristic
curves (ROC; Hanley & McNeil, 1982; Zweig & Campbell, 1993). The Area Under the
ROC Curve (AUC) value indicates the model accuracy (Fielding & Bell, 1997; Lobo
et al., 2008). For random prediction, AUC is 0.5. The main advantage of ROC analysis
is that the AUC provides a single measure of model performance, independent of any
particular choice of threshold.
In this work a bootstrap replicated run has been performed to do multiple runs (100)
for the same species; through this method the training data are selected by sampling with
replacement from the presence points (random test samples equal to 25%), with the number of samples equalling the total number of presence points (Phillips & Dudík, 2008).
Landscape Metrics
In order to analyse the pattern of the relationship between habitat suitability for the
three selected species and the landscape structure, 1000 random points have been
Table 1. Computed landscape metrics (Elkie et al., 1999; McGarigal & Marks, 1994)
Complexity
Index
AWMSI
Area weighted mean shape index
ED
Edge density
MPAR
Mean perimeter-area ratio
MPE
Mean patch edge
MSI
Mean shape index
Fragmentation
Index
MPS
Mean patch size
PSSD
Patch size standard deviation
NUMP
Number of patch
H
Shannon diversity index
Description
Shape complexity adjusted for shape size; equals the sum,
across all patches in the landscape, of the means shape
index multiplied by the proportional abundance of the patch.
Amount of edge relative to the landscape area; equals the
sum of the lengths of all edge segments involving the
corresponding patch type, divided by the total landscape
area.
The sum of all perimeters divided by the total area.
Average amount of edge per patch; equal the sum of all
patch perimeters within a landscape divided by the number
of patches.
The sum of each patch’s perimeter divided by the square
root of patch area for all patches (landscape level), and
adjusted against a square standard, then divided by the
number of patches.
Description
Size of individual land cover patches averaged over all
patches of a given class.
Is a measure of absolute variation; it is a function of the
mean patch size and the difference in patch size among
patches.
Number of patches on a landscape.
Equals minus the sum, across all patch types, of the
proportional abundance of each patch type multiplied by the
ln of proportion of the landscape occupied by each patch
type.
214 V. Amici et al.
selected and buffer areas around each random point, with a radius proportional to the
home range of the target species, have been created (Dauber et al., 2003; Doreen et al.,
2005).
Landscape structure was estimated through nine fragmentation and complexity landscape metrics (Table 1), computed, on the basis of the Corine Land Cover level IV
(APAT, 2005), using the Patch Analyst tool of ArcView software package (Elkie et al.,
1999). The complexity indices measure parameters that define the complexity of the
eco-mosaic and the presence of ecotones, while the fragmentation metrics measure the
fragmentation pattern in its various components (Bennett, 2003).
Through overlay operation in a GIS software (Quantum GIS), for each random point,
the landscape metrics values have been associated with the probability of occurrence
values of the Maxent output maps.
Then, we applied a Spearman rank correlation test (Zar, 1999) within the R software
(R Development Core Team, 2010) in order to test the significance (p-value) and the
correlation coefficient (rho) between landscape metrics and suitability or probability of
occurrence values.
Results
After calculating the correlation test we obtained a dataset of 14 variables to be used as
environmental layers in the model: geomorphology (altitude—ALT, slope—SLO, solar
radiation—SOR), land use (artificial areas—AA, croplands and pastures—CP, forests—
FO, shrublands and maquis—SM, grasslands, moors and heathlands—GH, water
bodies—WB), disturbance (road distance—ROD, distance from urban areas—DUA),
Figure 2. Receiver operating characteristic (ROC) curve for the Muscardinus avellanarius model,
averaged over the replicate runs.
Habitat Suitability and Landscape Structure 215
Figure 3. Receiver operating characteristic (ROC) curve for the Martes martes model, averaged
over the replicate runs.
Figure 4. Receiver operating characteristic (ROC) curve for the Mustela putorius model,
averaged over the replicate runs.
216 V. Amici et al.
climate (mean minimum winter temperature—MWT, mean maximum summer temperature—MST, mean annual rainfall—MAR).
The Maxent model predicted potential suitable habitats for the three selected species
showing high average training AUC and low standard deviation values for the replicate
runs: 0.920 and 0.016 for the Muscardinus avellanarius model, 0.983 and 0.006 for the
Martes martes model, 0.935 and 0.017 for the Mustela putorius model. The ROC
curves for the three target species data, averaged over the replicate runs, are shown in
Figures 2–4.
The performed Maxent model resulted in three suitability maps showing probability
of occurrence for each target species (Figures 5–7).
The Maxent model’s internal test of variable importance showed that the variables
concerning the land use and disturbance are the most important and useful variables in
explaining potential habitat suitability. In particular, artificial areas (AA), forests (F) and
croplands and pastures (CP) showed the highest relative contributions to the Muscardinus avellanarius model (Table 2). Forests (F), road distance (RD) and artificial areas
Figure 5. Point-wise mean of the 100 output grids for the Muscardinus avellanarius model.
Habitat Suitability and Landscape Structure 217
Figure 6. Point-wise mean of the 100 output grids for the Martes martes model.
(AA) were the most important predictors of Martes martes’s habitat distribution model
(Table 2), while forests (F), water bodies (WB), and mean minimum winter temperature
(MWT) were the most important predictors of habitat distribution in the Mustela putorius model (Table 2). These variables presented the higher gain (contained most information) compared to other variables.
The Spearman rank correlation test results showed a significant correlation between
landscape structure and habitat suitability (Table 3), with the exception of shape metrics
in the model for Mustela putorius. In particular, metrics related to patch size (MPS and
PSSD) showed high and positive rho values while the number of patch metric (NUMP)
showed a high negative correlation with habitat suitability. Metrics related to the
presence of edges (ED, MPAR, MPE) resulted in a high and negative correlation with
species probability of occurrence values. Shape metrics (MSI, AWMSI) and Shannon
index (H) showed low and negative rho values.
218 V. Amici et al.
Figure 7. Point-wise mean of the 100 output grids for the Mustela putorius model.
Discussion and Conclusions
Species distribution modelling has become increasingly popular in recent years among
researchers. It has been used to address a wide span of different problems at various
scales, with a range of different species occurring in different geographic areas (Cayuela
et al., 2009; Guisan & Thuiller, 2005; Kumar & Stohlgren, 2009).
As also evidenced by the results of this paper, there are several advantages of using
species distribution modelling to support conservation planning in complex landscapes,
especially when a great amount of field data are not available. In fact, maps based only
on field occurrences do not provide information on the likelihood of species occurrence
in areas that have not been surveyed (MacKenzie, 2006). Thus, accurate distribution
maps should be used to make field inventories more efficient and effective and support
nature reserve selection. Our results confirm the important role that distribution models
can have in highlighting the areas where targeted species or habitat type is most likely to
be found, and showing where to commit the limited available resources for inventories.
Habitat Suitability and Landscape Structure 219
Table 2. Relative contributions of the environmental variables to the Maxent models (%)
Variable
Muscardinus
avellanarius
Martes
martes
Mustela
putorius
6.1
22.5
9.8
13.1
5.2
3.9
7.6
5.3
5.3
4.4
4.9
3.9
5.9
2.1
100
1.5
8.5
5.2
33.9
8.7
4
7.1
3.5
5.0
5.2
2.8
13.2
0.6
0.8
100
5.2
4.1
2.2
16.6
6
10.3
7.6
16.2
11.6
2.2
7.9
4.3
4.2
1.6
100
Altitude
Land use 1: Artificial areas
Land use 2: Croplands and pastures
Land use 3: Forests
Land use 4: Shrubland and maquis
Land use 5: Grasslands, moors and heathland
Land use 6: Distance from urban areas
Land use 7: Water bodies
Mean minimum winter temperature (1990–2000)
Mean maximum summer temperature
Mean annual rainfall (1990–2000)
Road distance
Slope
Solar radiation
Table 3. Spearman rank correlation results for the three developed models
Muscardinus
avellanarius
Martes martes
Landscape metrics
NUMP
MPS
PSSD
ED
MPAR
MPE
MSI
AWMSI
H
p-value
p
p
p
p
p
p
p
p
p
<
<
<
<
<
<
<
<
<
0.001
0.001
0.001
0.001
001
0.001
0.01
0.01
0.01
rho
0.69
0.69
0.60
0.60
0.53
0.49
0.19
0.19
0.12
p-value
p
p
p
p
p
p
p
p
p
<
<
<
<
<
<
<
<
<
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.01
Mustela putorius
rho
0.67
0.67
0.59
0.61
0.51
0.53
0.33
0.33
0.17
p-value
p
p
p
p
p
p
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
< 0.001
ns
ns
p < 0.001
rho
0.42
0.37
0.21
0.29
0.21
0.12
0.02
0.02
0.14
In this context, the choice of the approach in the implementation of the model
becomes crucial. Recent studies have successfully applied niche-based models using
presence-only data to map habitat in space (Pearson et al., 2007; Phillips et al., 2006).
By isolating the niche relationships in occurrence data, presence-only habitat models
supply knowledge of species’ environmental and spatial distributions with less dependence on observational factors (Elith et al., 2006). The models generated by Maxent
have a ‘natural’ probabilistic interpretation, giving a smooth gradation from most to
least suitable conditions.
The distribution models developed in this work showed a good predictive power
(with random sets of evaluation presence records). The resulting maps describe currently suitable habitat rather than actual target species occupancy given that, as already
mentioned, areas that are delineated as suitable may in fact be unoccupied due to
factors like human disturbance or recent local extinctions. At any rate, the analysis of
the habitat distribution spatial patterns can be of support in the understanding of the
220 V. Amici et al.
ecology of our target species, stimulating an objective approach in addition to policy
and administrative constraints.
As expected, the presence of forest habitats was an important determinant of suitability for the three selected target species with the highest values for Martes martes, as
this species is associated with the presence of mature forested environments (Clevenger,
1994; Kranz et al., 2008). Martens are generally thought to require large areas of
mature forest rather than mixed landscapes of different-aged stands (see Helldin, 2000;
Potvin et al., 2000). Analogously to other small–medium-bodied mammals, this species
shows a fragmentation sensitivity due to their specific ecological traits (e.g. high trophic
level, medium–low dispersion capacity, high home range requirement; Amici & Battisti,
2009; review in Spinozzi et al., 2012). The sensitivity to habitat fragmentation is also
shown by the Spearman rank correlation results (Table 3); analyzing the obtained values, we may generally affirm the results of McGarigal & McComb (1995) in finding
that fragmentation components are generally more important than complexity ones (see
also Fahrig, 2003; Fischer et al., 2004). We have a high negative correlation with the
number of patches and edge density and a high positive correlation for the landscape
metrics concerning the patch size, showing a high habitat suitability of Martes martes.
In Martes martes (and also in other mustelidae), spatial heterogeneity (also induced by
human-induced habitat fragmentation) and physical structure of the environment (both
of them surrogates of habitat ‘quality’), have been suggested as important factors shaping spatial requirements and consequent demographic structure of local populations
(Rondinini & Boitani, 2002; Smith & Schaefer, 2002; for a vicariant species, the American marten, see also Buskirk et al., 1998; Hargis et al., 1999). For example, in landscape mosaics differences in soil and vegetation induce markedly different population
density in Martes martes that locally may show a patchy structure (Sidorovich et al.,
2005).
In the Muscardinus avellanarius model, the high contribution value of the ‘artificial
areas’ is conspicuous. Indeed, this rodent inhabits edge wooded areas with a dense
shrubby understorey and it is very sensitive to high human disturbance (Amori et al.,
2008). Generally, dormice are reluctant to cross gaps due to habitat fragmentation
(Bright, 1993, 1998; Bright & Morris, 1994; Russell et al., 2003) and the activity patterns in this species are highly related to habitat heterogeneity and disturbances at
patch/landscape scale (Capizzi et al., 2002; Panchetti et al., 2004). Moreover, habitat
loss has been identified as the major driver of distribution patterns in this rodent and
structural connectivity at landscape scale (e.g. hedgerow networks) has been shown to
play an important role in determining their distribution (for central Italy, see Mortelliti
et al., 2011). This is confirmed by the results of the correlation analysis, with the landscape metrics showing as the occurrence of Muscardinus avellanarius seems to be
highly affected by the fragmentation metrics.
Water bodies and semi-natural land use classes emerged as important determinants
for Mustela putorius distribution; this can be explained by the fact that this species
prefers living near fresh water bodies, in wet areas, and its presence is related to
agricultural areas characterised by a high landscape heterogeneity (Benton et al., 2003).
Key components of the habitat heterogeneity required by Mustela putorius are the noncropped areas (including field margins), hedges, ponds and ditches, abandoned pastures
and non-intensive tree crops. These habitats generally host high density populations and
Habitat Suitability and Landscape Structure 221
a high amount of species since adjacent ecosystems experience flows of energy, nutrients and species across their mutual boundary (Murcia, 1995; Rondinini et al., 2006).
Nevertheless, although a natural heterogeneity may favour the suitability for Mustela
putorius, the human-induced heterogeneity (linked to the habitat fragmentation process)
may represent a greater risk for this species (Amici & Battisti, 2009; Blandford, 1987).
Analogously to other intrinsic and extrinsic factors (social organisation, sex, climate
and environmental seasonality, prey availability), landscape heterogeneity affects the
home range and population density in a different way for medium–large mammals (e.g.
Martes martes and Mustela putorius) (Birks, 1999; McLoughlin & Ferguson, 2000).
For all three target species, habitat suitability appears particularly strongly associated
with the level of fragmentation in the buffer areas for the three built models. In regions
with large naturally forested areas, as is the case in the study area, levels of fragmentation are positively correlated with the degree of human disturbance (Scott et al., 2006;
Turner et al., 2001). In fact, the reduction of habitat area, due to human induced fragmentation, often results simultaneously in more irregular shaped patches, greater patch
number and lower mean patch size (with higher edge effect due to local disturbances;
Fahrig, 2003). Additionally, the low Spearman’s rho values for shape indices is
probably due to the Corine Land Cover resolution, with a minimal mapping unit
(MMU) which does not allow adequate individuation of edges and shapes of the habitat
features.
Through our results it is possible to highlight that, despite species’ responses to landscape structure are generally complex, the research proposal methodology has great
potential in increasing the explanatory capacity of distribution models and supporting
the understanding of habitat suitability patterns. The effectiveness of this methodology
depends on the possibility of combining the information provided by the environmental
predictors contribution to the model with information on how suitability could be
related to landscape configuration (through the incorporation of landscape metrics).
Undoubtedly, maintenance or preservation of large patches of suitable habitat will
remain one of the priorities for the conservation of mustelids, and in particular the medium–large-bodied species that inhabit boreal forests (e.g. genus Martes and Mustela;
Helldin, 2000; Potvin et al., 2000; Rondinini & Boitani, 2002). Moreover, the potential
habitat distribution maps for the three species considered in this study could be used in
environmental planning and land use management to identify top-priority survey sites,
or to set habitat restoration priorities. In addition, the availability of suitability/distribution maps developed with a relatively fine grain (30 m), in comparison to the average
home range of the species, may allow us to focus on species-specific natural habitats,
allowing a spatial prioritisation of conservation actions.
The approach proposed in this work, could be extended to other fragmentation sensitive and mosaic species. Indeed, habitat heterogeneity (and landscape structure) has
important implications for the conservation of medium-large-bodied mammals and it is
strictly related to the history of local human-induced and natural disturbances (Turner,
1987). In particular, the cumulative effects of the natural landscape heterogeneity, in
combination with increased (anthropogenic) landscape fragmentation and local
disturbances, need to be considered if conservation strategies and management measures
are focused on these mammals (McLoughlin & Ferguson, 2000).
The use of this inductive modelling approach may support environmental planning
strategies in landscapes of conservation concern focused on single target species (as the
222 V. Amici et al.
species selected in this study). Moreover, the role of umbrella of target species here
selected may develop strategies on landscape conservation on a larger number of target
of biodiversity.
Concluding, the methodology presented here could be used for an integrated
approach considering the landscape configuration in modelling species potential distribution. Combining classical environmental information with landscape structure in analysing habitat suitability for species of conservation concern (and for wider targets) can
be used to guide conservation efforts and landscape management practices.
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