LAND-COVER CHANGE AND THE FUTURE OF THE APENNINE BROWN BEAR: A PERSPECTIVE FROM THE PAST
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Journal of Mammalogy, 89(6):1502–1511, 2008
LAND-COVER CHANGE AND THE FUTURE OF THE
APENNINE BROWN BEAR: A PERSPECTIVE
FROM THE PAST
ALESSANDRA FALCUCCI,* LUIGI MAIORANO, PAOLO CIUCCI, EDWARD O. GARTON, AND LUIGI BOITANI
Department of Animal and Human Biology, Sapienza Università di Roma, Viale dell’Università 32, 00185
Rome, Italy (AF, LM, PC, LB)
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Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83843, USA (AF, LM, EOG)
The Apennine brown bear (Ursus arctos marsicanus) is an endangered subspecies endemic to Italy, where
a small population, estimated at 40–50 bears, inhabits a human-dominated landscape. Although little is known of
the ecology of this population, habitat loss and fragmentation often has been considered one of the main threats
for small and endangered populations. To assess habitat availability at the landscape scale, we used a distribution
model to compare historical, present, and future land-cover suitability for the Apennine brown bear population in
central Italy. The 4 models are based on 3 existing land-cover maps (1960, 1990, and 2000) and 1 simulated map
for 2020, obtained from a cellular-automata Markov-chain land-transition model. We also compared changes in
human population density as a surrogate for human pressures on bear habitat, and we measured the contribution
of protected areas to the bear’s conservation. Our results show that, at the landscape level and assuming that
current human population trends continue in the future, land-cover suitability does not seem to be an issue or
priority. The current negative trend of this population, despite opposite trends in land-cover suitability, suggests
that conservation efforts should focus more on direct actions aimed at reducing human-caused mortality and
enhancing population expansion into suitable unoccupied areas.
Key words: Abruzzo, bear conservation, geographic information system, habitat modeling, Italy, landscape dynamics,
Ursus arctos marsicanus
The brown bear (Ursus arctos) was widespread in the entire Taglianti 2003) and it is restricted to roughly 5,000–8,000 km2
holarctic region until the beginning of 1800s when, facing (Fig. 1). However, the most densely and consistently occupied
persecution and habitat destruction, its population decreased area (1 bear/50–80 km2—Lorenzini and Posillico 2000) is only
markedly (Swenson et al. 2000). Nowadays, in Europe, it is 1,500–2,500 km2 (Fig. 1), mainly in Abruzzo-Lazio-Molise
abundant only in the eastern and northern parts (Zedrosser et al. National Park (hereafter, Abruzzo National Park) and sur-
2001), whereas in western Europe it is facing high risks of rounding areas (Bologna and Vigna-Taglianti 1992; Boscagli
extinction and is restricted to small, isolated, and endangered 1999; Meriggi et al. 2001; Posillico et al. 2004). In other
populations (Swenson et al. 2000; Taberlet et al. 1995). In parts of the central Apennines, the presence of the species is not
Italy, the brown bear has seen a progressive reduction in its stable and densities are very low (Ciucci and Boitani 2004;
range since the 1600s due to legal hunting (before 1938), Posillico et al. 2004).
poaching, and habitat destruction (Fabbri et al. 1983; Febbo The subspecies U. a. marsicanus is protected by law and
and Pellegrini 1990). Today it is restricted to 2 populations in Abruzzo National Park, along with other adjacent protected
the Alps and 1 in the Apennines (Boitani et al. 2003; Linnell areas (PAs), has been created to secure the bear’s conservation.
et al. 2007). The central-Apennine population is a subspecies The Apennine brown bear is considered endangered by the
endemic to Italy (U. a. marsicanus, hereafter, the Apennine Italian World Wildlife Fund red list (Bulgarini et al. 1998), and
brown bear—Loy et al. 2008; Randi et al. 1994; Vigna- critically endangered under Criterion D by the International
Union for Conservation of Nature European Mammal Assess-
ment (International Union for Conservation of Nature 2007).
* Correspondent: alessandra.falcucci@uniroma1.it Moreover, Swenson et al. (2000) considered the subspecies as
highly threatened; Convention on International Trade in
Ó 2008 American Society of Mammalogists Endangered Species of Fauna and Flora includes the Apennine
www.mammalogy.org brown bear in its Appendix II, the European Community listed
1502December 2008 FALCUCCI ET AL.—LAND-COVER CHANGE AND APENNINE BEARS 1503
Habitat loss and degradation have been considered 2 of the
main reasons for the Apennine brown bear’s decrease (Bologna
and Vigna-Taglianti 1992; Fabbri et al. 1983; Febbo and
Pellegrini 1990), and are considered among the major long-
term threats for its survival (Swenson et al. 2000). Nonetheless,
no formal evaluation of the status and trends of the bear habitat
has been undertaken in the region. Simply assuming that
habitat loss is (and has been) the main cause of bear population
decrease might diminish the potential role of other threats (e.g.,
human-induced mortality and low recruitment rates). Thus,
conducting a large-scale habitat evaluation is important
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because any conservation strategy for demographic recovery
must consider space and habitat availability, possibly with
explicit reference to habitat changes over time.
Our primary goal is to test the hypotheses that land-cover
suitability for the Apennine brown bear in central Italy is
declining and that the current system of PAs is insufficient to
ensure the persistence of the subspecies. In particular, we
assessed historic (1960s and 1990) to current (2000) changes in
land-cover suitability for Apennine brown bears and used these
changes to project likely effects of future (2020) land-cover
changes on this population. We also evaluated changes in
human population density for the same time frames as
a surrogate for human encroachment into the bear’s habitat.
FIG. 1.—A) Distribution of brown bears (Ursus arctos) in Italy and In addition, we investigated distribution and location of
in the surrounding countries (modified from Large Carnivore
suitable areas in central Italy that are still unoccupied by bears
Initiative for Europe 2007; http://www.lcie.org/). B) Distribution of
and where individuals from the core population might
brown bears in the study area and existing protected areas.
eventually expand in the near future. Our aim is to facilitate
conservation strategies based on metapopulation dynamics,
the bear as endangered in the Habitat Directive in 1992, and the providing spatially explicit references for intensive manage-
international community classified the bear as strictly protected ment actions (e.g., conflict resolutions and population aug-
in the Bern convention in 1979. mentation), and habitat restoration. This further allows us to
The central-Apennine population has been completely measure the potential contribution of existing and proposed
isolated from other bear populations for 400–600 years PAs to the goal of large-scale and long-term conservation for
(Lorenzini et al. 2004; Randi et al. 1994; but see Loy et al. the Apennine brown bear.
[2008], who suggested a postglacial separation), and current
population size has been estimated at 43 individuals (95%
confidence interval: 35–67—Gervasi et al. 2008), which may MATERIALS AND METHODS
correspond to an effective population size of 4–10 adult Our study area (22,000 km2) corresponds to the approximate
females (Ciucci and Boitani 2008), below the number that is range of the Apennine brown bear in 1800 (Boitani et al. 2003;
required for a viable population (Wiegand et al. 1998; Fig. 1). We built a distribution model for the Apennine brown
Wielgus 2002; but see Sæther et al. 1998). Nevertheless, bear using 4 geographic information system layers: a digital
Lorenzini et al. (2004) did not reveal any indications of elevation model (cell size ¼ 75 m), and 3 land-cover maps,
inbreeding depression among 30 individually genotyped including the Land-Cover 1960 (LC1960), the CORINE Land-
bears. Moreover, the Apennine brown bear lives in Cover 1990 (CLC1990), and the CORINE Land-Cover 2000
a human-dominated landscape, suffers consistent human- (CLC2000). The LC1960 (scale 1:200,000; 22-class legend)
caused mortality (Posillico et al. 2002; L. Gentile, Veterinary was produced from 1956 to 1968 by the National Research
Service, Abruzzo-Lazio-Molise National Park, pers. comm.), Council, Rome, Italy. The CLC maps (scale 1:100,000; 44-
and lacks a coordinated conservation strategy (Swenson et al. class legend) were produced in 1990 and in 2000 by the Italian
2000) to overcome the existing political and administrative Ministry for the Environment (Rome, Italy) for the European
fragmentation (i.e., the existence of more than 1 administra- Community (Bruxelles, Belgium).
tive entity issuing laws and regulations on the same bear To obtain 3 thematically homogeneous geographic in-
population). Such a strategy is difficult to achieve in the formation system layers, we reclassified the 2 CLC maps and
absence of reliable knowledge on the status, ecology, and the LC1960 map (Table 1), slightly modifying the legend
threats to the population. Thus, the Apennine brown bear proposed by Falcucci et al. (2007) to include more thematic
faces serious problems and needs significant efforts to details for agricultural areas and forests. To obtain 3 spatially
achieve successful conservation. homogeneous layers, we transformed the land-cover maps from1504 JOURNAL OF MAMMALOGY Vol. 89, No. 6
vector to raster, assigning to each map the same origin, extent, TABLE 1.—Scores assigned to each land-cover class for the
and cell size (200 200 m). Cell size was chosen to be con- distribution model and for the possible alternative models (0 ¼
sistent with the coarser resolution of the LC1960. The digital land-cover class that does not support the presence of the bear; 1 ¼
elevation model was resampled to match the resolution and the land-cover class that fulfils partial resource requirements in terms of
extent of the land-cover layers. food, cover, and water; 2 ¼ land-cover class that fulfils resources
requirements at a suboptimal level; 3 ¼ land-cover class that fulfils
For the period 1960–2000, we used spatially explicit data
optimal resources requirements). A dash (—) indicates that no
on the human population based on national censuses (Italian alternative score has been considered.
Institute of Statistics; www.istat.it). Existing PAs and the
NATURA2000 network (a network of conservation areas Land-cover class Model Alternative score
proposed by the European Community for the conservation of Artificial 0 —
species and habitat listed under the Habitat Directive) were Non-irrigated arable lands 1 0
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obtained from Maiorano et al. (2006, 2007). Irrigated arable lands 0 —
The Forestry Service (Ufficio Territoriale per la Biodiversità Vineyards 0 —
Wooden cultivations 2 1
di Castel di Sangro, Castel di Sangro, L’Aquila, Italy) provided
Olive groves 0 —
407 locations of Apennine brown bears, including hair Pastures 1 —
collected systematically through baited traps, scats collected Complex agricultural areas 1 0
opportunistically along trails, specimens collected occasional- Agricultural areas with natural vegetation 2 —
ly, and intensive surveys within patches of Rhamnus alpinus, Broadleaf forests 3 —
Coniferous forests 2 1
that is, seasonal aggregation sites for bears (Randi et al. 2004,
Mixed forests 3 2
2005). The data were collected from 2000 to 2003 within Natural prairies 1 —
a European Union LIFE project following standard protocols Moors 2 —
(Woods et al. 1999), inside the park and within its buffer zone, Sclerophyllous vegetation 2 1
and met guidelines approved by the American Society of Forestshrubs transitional areas 3 2
Beach and dunes 0 —
Mammalogists (Gannon et al. 2007). Laboratory protocols
Scarcely vegetated areas 1 —
were implemented to obtain reliable individual genotypes (42 Marshes 0 —
individual genotypes were identified—Randi et al. 2005) and Bare rocks 1 —
molecular sex determination (19 females and 23 males). We Rivers 0 —
retained only 304 locations (those with .95% probability of Lakes 0 —
being bears on a genetic basis), of which 33% occurred
between May and August (spring to early hyperphagia, as
defined by Swenson et al. [2000]), and 67% between percentage of the study area classified in the same land-cover
September and December (late hyperphagia). class in all the 100 runs of the CA_MARKOV procedure.
Land cover in 2020.— To project land cover in 2020, we Further details on the CA_MARKOV algorithms can be found
used the 2 CLC maps in a combined cellular-automata in Eastman (2001).
Markov-chain multicriteria–multiobjective land allocation Land-cover–suitability models.— To model the land-cover
land-cover prediction procedure (CA_MARKOV procedure suitability for the Apennine brown bear, we used information
in IDRISI3.2—Eastman 2001). The CA_MARKOV algorithm available on the ecology of the species (Boitani et al. 2003;
is based on the assumption that future land-cover changes can Maiorano et al. 2006; Posillico et al. 2004; Swenson et al.
be predicted using past land-cover changes (Eastman 2001). 2000) to build 1 deductive model (DM, sensu Corsi et al.
We used CLC1990 and CLC2000 to obtain a transition-area [2000], i.e., expert and literature based) for each time frame
file, quantifying changes from each land-cover category in (i.e., 1 DM for 1960, 1 for 1990, 1 for 2000, and 1 for 2020).
1990 to each other category in 2000. The suitability of each We chose a deductive approach because it allows for the
pixel for each land-cover type is determined on the basis of generalization of species–habitat relationships over large areas,
a set of land-cover–suitability maps, 1 for each land-cover providing a synthesis of the available knowledge (Johnson and
type. We built each land-cover–suitability map using Maha- Gillingham 2004), and because points of presence were
lanobis distance statistics (De Maesschalck et al. 2000) based available only for 2000.
on topographic and anthropogenic layers (aspect, elevation, Following Clevenger et al. (2002) and Maiorano et al. (2006),
slope, main and secondary road densities, and distance to main land cover and elevation were used as surrogates of bear habitat
and secondary roads). We used a contiguity filter to down- qualifiers. The 2 layers can be easily related to the existing
weight the land-cover suitability of pixels far from existing knowledge on the ecology of the population, and, finally, digital
areas of each land-cover class, thus giving preference to maps of these variables exist (or can be obtained through
contiguous suitable areas (Eastman 2001). simulations) for all of the 4 time frames that we considered.
Using CLC2000 as the starting point for the land-cover For each time frame, our modeling approach involved 2
change projection in 2020, we ran the entire procedure 100 steps: reclassification of the 2 environmental layers based on
times and we assigned the value that occurred most often for the species’ land-cover and elevation preferences, and com-
each pixel to the final 2020 land cover. Moreover, to evaluate bination of the 2 reclassified layers to obtain a final suitability
the stability of the land-cover projection, we measured the score for each 200 200-m cell in the study area. In the 1stDecember 2008 FALCUCCI ET AL.—LAND-COVER CHANGE AND APENNINE BEARS 1505
TABLE 2.—Combinations of elevation and land-cover scores to model whose predictions are consistent with the data set, and
obtain the suitability scores for the deductive models. Elevation scores with values close to 0 indicating that the model is not different
based on expert opinion and literature data: 0 ¼ out of the usual from a chance model. The index is built calculating the ratio of
elevation range of presence (,500 m); 1 ¼ inside the usual elevation predicted frequency versus the expected frequency for each
range of presence (from 500 m to the maximum elevation possible in suitability class. The predicted frequency for a given suitability
the study area); 2 ¼ inside the ‘‘core’’ elevation range (from 800 m to
class is calculated as the number of evaluation points predicted
1,800 m). See Table 1 for land-cover scores.
by the model to fall in that suitability class; the expected
Elevation frequency is calculated as the percentage of evaluation points
Land cover 0 1 2
expected in the suitability class on the assumption of a random
distribution across the study area.
0 Unsuitable Unsuitable Unsuitable
To calculate the predicted frequency, we built for each
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1 Unsuitable Low suitability Low suitability
2 Unsuitable Low suitability Medium suitability validation point a 500-m circular buffer and we assigned to
3 Unsuitable Medium suitability High suitability each point the suitability class with the highest share inside
the buffer. Alternatively, if 2 or more suitability classes had the
same share, the highest suitability was chosen. To calculate the
step, the land-cover map was reclassified into 4 categories expected suitability we used 20,000 random locations (corre-
(Table 1), whereas the elevation map was reclassified into 3 sponding to roughly 0.9 points/km2, a density similar to that
categories (Table 2). In the 2nd step, the scores available for measured for the validation points), and we assigned a suit-
land-cover classes and elevation were integrated to obtain the ability class to each location following the same procedure
final suitability scores for the DM (Table 2). outlined for the points of presence.
Land-cover suitability and human population density.— We No validation was possible for the DMs of time frames
investigated the relationship between land-cover suitability other than 2000, because no presence data sets were available.
and human population changes. For each administrative unit However, we performed a sensitivity analysis (Ray and
(n ¼ 592) we calculated changes in human population density Burgman 2006) to evaluate the extent of the deviations in the
from 1960 to 2000. Administrative units were classified into results by using different suitability scores (see Table 1) and
2 groups: those where human population increased and those changing the elevation ranges (original ranges 6 100 m). We
where human population decreased. For each unit we calculated ran a total of 639 different models and measured the correlation
the percentage of area occupied by each suitability class and existing among them and the final DM using Cramer’s V
tested the significance of the differences using a median test. (significance tested with a chi-square statistic—Ott et al. 1983).
Land-cover suitability and PAs.— We measured land-cover
suitability inside PAs and inside NATURA2000 and tested the
difference in suitability with the rest of the study area using RESULTS
a median test. In particular, we drew 1,000 random points in Land cover in 2020.— The 2020 land-cover change pro-
PAs and 3,500 outside (both corresponding to 0.2 points/km2). jection was performed 100 times. With just 5 simulations, more
For each point, we built a circular buffer with a 500-m radius than 97.7% of the study area was always classified in the same
and measured the area occupied by the different suitability land-cover class, and after 25 simulations the percentage did
classes. The same procedure was used considering PAs plus the not change markedly (96.4%, 96.1%, and 96.05% after 25, 50,
NATURA2000 network. and 100 simulations, respectively). The remaining areas of
Large-scale connectivity and landscape indices.— To mea- uncertain assignment (roughly 4% of the study areas) were
sure large-scale connectivity between the area of stable small (median size ¼ 1 ha) and located at the boundaries
presence (Fig. 1) and the rest of the study area, we ran a between land-cover classes.
least-cost–path analysis (Walker and Craighead 1997) using Land-cover suitability models.— Most of the high- and
suitability scores as proxies for movement costs (with lower medium-suitability areas for the Apennine brown bear are in
suitability implying higher costs). To monitor the changes oc- the internal, mountainous parts of the study area (Fig. 2),
curring in the landscape structure and to numerically quantify whereas most of the low-suitability and unsuitable areas are in
the least-cost–path analysis, we measured the following land- lowlands, roughly corresponding to areas with high human
scape indexes for each DM (over the entire study area, over population densities. From 1960 to 1990 the high-suitability
the PAs, and over the non-PAs): percentage of the landscape, classes increased (Figs. 2 and 3). In particular, unsuitable areas
number of patches, largest patch index, euclidean nearest covered 24% of the study area both in 1960 and in 1990.
neighbor median distance, and normalized landscape shape However, low-suitability areas occupied almost 50% of the
index (McGarigal and Marks 1995; Turner et al. 2001). study area in 1960 and 29% in 1990. The opposite was true for
Validation and sensitivity analyses.— To evaluate the DM higher suitability classes that increased in 1990. The pattern of
developed for 2000, we calculated the Boyce index (Boyce suitability did not change markedly for 1990, 2000, and 2020
et al. 2002; Hirzel et al. 2006) using the 304 available points (Figs. 2 and 3).
of presence. The index goes from 1 to 1, with negative values Land-cover suitability and human population density.—
indicating a model that predicts poor-quality areas where Median human population density decreased from 1960 to
presence is more frequent, with positive values indicating a 2000 (Fig. 4). In particular, human population decreased in 4871506 JOURNAL OF MAMMALOGY Vol. 89, No. 6
Land-cover suitability and PAs.— Protected areas covered
.23% of the study area (5,230 km2). Including also the
NATURA2000 network, the percentage of PAs exceeded 38%
of the study area (851,530 km2). Compared to the rest of the study
area, PAs hosted a higher percentage of high- and low-suitability
areas, and a lower percentage of unsuitable and medium-
suitability areas (P , 0.0001; Fig. 5), and the same results were
obtained combining PAs and the NATURA2000 network.
Large-scale connectivity and landscape indices.— Accord-
ing to our least-cost–path analysis, in 1960 the areas with a
stable presence of the bears appeared to be fairly well con-
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nected to the southern portion of the study area, whereas
northward connections were limited. In 1990, the entire land-
scape was much more homogeneous, and large-scale connec-
tivity increased widely, with barriers to movement of the
animals existing only in correspondence to intensively culti-
vated areas. In 2000 and in 2020, the connectivity within the
landscape was similar to that estimated in 1990 (Fig. 6) but
with a clear trend toward increasing levels of connectivity.
The landscape indices are consistent with the least-cost–path
analysis, suggesting decreasing fragmentation of habitat for
brown bears. Across time, most landscape indices show dif-
FIG. 2.—Deductive models for the Apennine brown bear (Ursus ferent trends for the unsuitable and the low-suitability areas
arctos marsicanus) in 1960, 1990, 2000, and 2020. versus medium- and high-suitability areas (Fig. 7). In 1960,
unsuitable areas were highly contiguous (low number of
(82.3%) administrative units from 1960 (median ¼ 0.65 patches, high percent of the landscape, and low normalized
inhabitants/ha) to 1990 (0.45 inhabitants/ha) and in 398 landscape shape index), but in 1990, the number of patches for
(67.2%) units from 1990 to 2000 (0.44 inhabitants/ha). unsuitable areas increased and the largest patch index de-
Administrative units where human population decreased from creased, indicating an increasing fragmentation. Projections
1960 to 1990 had a higher percentage of medium- and high- from 2000 to 2020 suggest a tendency toward increasing
suitability areas when compared with administrative units fragmentation of unsuitable areas, with normalized landscape
where human population increased (P , 0.0001). The same shape index, percent of the landscape, and largest patch index
was true for 1990–2000. that should remain almost unchanged, whereas number of
FIG. 3.—Percentage of the landscape (PLAND) of our study area in central Italy in the 4 suitability classes from 1960 to 2020.December 2008 FALCUCCI ET AL.—LAND-COVER CHANGE AND APENNINE BEARS 1507
whereas number of patches, normalized landscape shape index,
and euclidean nearest neighbor distance decreased, indicating
a lower fragmentation. We revealed the same tendency for
medium- and high-suitability areas for both 2000 and 2020.
Overall, by contrasting PAs and the rest of the study area
(results not shown), we revealed a greater fragmentation for
unsuitable areas and a lower fragmentation of high-suitability
areas inside PAs. However, no temporal trends of the landscape
metrics were detected between PAs and the rest of the study
area.
Validation and sensitivity analyses.— The Boyce index
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calculated for the 2000 DM was 0.8, indicating that the DM
is sufficiently consistent with the evaluation data set (bear
presence data points). As indicated by Cramer’s V (X 6 SD;
1960: 0.78 6 0.11; 1990: 0.85 6 0.06; 2000: 0.84 6 0.07;
FIG. 4.—Median human population density per administrative unit
from 1951 to 2001 (data source: Italian Institute of Statistics; http://
2020: 0.82 6 0.07; all P , 0.001) all 639 alternative DMs used
www.istat.it). for sensitivity analysis were not significantly different from the
final model.
patches should decrease and euclidean nearest neighbor
distance increase (Fig. 7). DISCUSSION
We revealed a similar pattern for low-suitability areas, which Although we checked for validity and stability of all the
in 1960 represented the prevalent class (36% of the landscape), models, we had no way of validating the 2020 land-cover
but appeared increasingly fragmented in 1990 (,30% of the projection, which is based on the assumption that the pattern
landscape, largest patch index , 5%, and both number of of land-use change observed during 1990–2000 will remain
patches and normalized landscape shape index increased with constant up to 2020. Although indications provided by the
respect to 1960). Projections from 2000 to 2020 suggest that 2020 projection should be interpreted cautiously, the resulting
patterns of low-suitability areas should stabilize or show output was very stable after 20 simulations, which indicates,
a tendency toward higher fragmentation (Fig. 7). at least, that if our assumptions hold, then our results are
Medium- and high-suitability areas showed different trends. consistent. We recognize that development projects (e.g.,
In 1960, the 2 classes represented only a minor portion of the tourist and ski resorts, roads, and wind farms) can drastically
study area and were relatively fragmented (Fig. 7). In 1990, change the landscape. Nevertheless, our 2020 landscape is
percent of the landscape and largest patch index increased, based on layers that represent proxies for the probability of
FIG. 5.—Percentage of protected areas (PAs) and nonprotected areas (No PAs) in central Italy occupied by the different suitability classes in
1960, 1990, 2000, and 2020.1508 JOURNAL OF MAMMALOGY Vol. 89, No. 6
www.regione.abruzzo.it), and therefore should not alter
significantly the pattern of land-use change in high-suitability
areas. We did not take into account the effects of climate
change on habitat availability for the bears. However, both
scale and time frames are too detailed to build realistic models
on climate change and our data set does not allow building
a reliable climatic envelope.
The DM for 2000 was validated using field data. The con-
cordance between true presence locations and suitability
predictions indicates that our deductive approach provides
a reliable synthesis of the species distribution in the study area
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(but consider that no evaluation of the specificity of our DM
was possible, given that no true absence point was available).
No validation was possible for the DM of the other time
frames, but sensitivity analyses showed extremely low
departures to changes in suitability scores, further supporting
the reliability of our DM.
Overall, our results suggest that large-scale availability of
areas suitable for the bears is not a major issue for their
conservation, provided that recent and current land-use trends
are maintained and that our DM is valid. Although increasing
habitat destruction is continuing today in many developing
countries (e.g., Sodhi et al. 2004), different trends have been
observed in many parts of Europe (Debussche et al. 1999;
Falcucci et al. 2007; Olsson et al. 2000). During the 20th
century, traditional agriculture, grazing, and forestry activities,
FIG. 6.—Estimated movement cost for the Apennine brown bear
following rural depopulation, became increasingly economi-
(Ursus arctos marsicanus) from the area of stable presence to the rest
of the study area from 1960 to 2020. cally nonviable (Cohen 2003; Olsson et al. 2000). As
a consequence, vegetation succession is progressing toward
future developments, thus minimizing the possibility for forest reestablishment and spread (Falcucci et al. 2007; Laiolo
unforeseen events. Moreover, most of the projects currently et al. 2004), and, at the same time, large carnivores are
considered will be implemented in areas of low suitability for increasing their ranges, at least in some areas of Europe (http://
the bears (Piano d’azione per la tutela dell’orso marsicano; www.lcie.org/).
FIG. 7.—Landscape indexes (NP ¼ number of patches; LPI ¼ largest patch index; ENNMD ¼ euclidean nearest neighbor median distance;
NLSI ¼ normalized landscape shape index [dimensionless]) calculated for the entire study area in 1960, 1990, 2000, and 2020.December 2008 FALCUCCI ET AL.—LAND-COVER CHANGE AND APENNINE BEARS 1509
Considering land-cover only, we showed that habitat seem appropriate to host a larger bear population and to allow
availability also has increased for Apennine brown bears, for the natural recolonization of its former range. Despite the
especially from 1960 to 1990. After 1990, we measured limited positive trends in suitability for the past 40 years, the bear
changes in habitat availability, probably because most of the population apparently continues to decrease (Ciucci and
remaining unsuitable areas are in lowlands, where agriculture Boitani 2008; International Union for Conservation of Nature
and urbanization are the dominant features, and human 2007; Linnell et al. 2007; Wilson and Castellucci 2006). At the
population did not, and most probably will not, decrease. landscape scale, the level of connectivity indicated by our
The least-cost–path analysis indicated that natural recoloni- model should be preserved from potential developments or
zation by bears of the northern part of the study area is possible, habitat alteration. As in the Greater Yellowstone Ecosystem
at least at the landscape scale and according to the variables (Gunther et al. 2004), direct conservation actions aimed at
we modeled. Once more, it is important to underline that our reducing human-caused mortality appear extremely urgent.
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results for 2020 do not account for future developments of
roads and highways, considered among the most important ACKNOWLEDGMENTS
barriers to movements by bears (Swenson et al. 2000). The Abruzzo-Lazio-Molise National Park administration, through
We have no direct validation of the least-cost–path analysis, its Scientific and Surveillance Services, and the Forestry Service
but our results are confirmed by bear sign (obtained by camera- (Ufficio Territoriale per la Biodiversità; Castel di Sangro) collaborated
in the collection of the data on bear presence used for model
traps, sightings, and scats) collected over the last 15 years in
evaluation. Funding for this project was partially provided by
the northern portion of the study area about 140 km from the
a Wildlife Conservation Society grant and by a Ministry of the
park (P. Ciucci, L. Carotenuto, P. Morini, and P. Forconi, pers. Environment (Directorate for Nature Conservation) grant to the
comm.), where the Apennine brown bear was considered ex- Department of Human and Animal Biology (Sapienza Università di
tinct by the 1930s (Boitani et al. 2003). It is therefore evident Roma). The Institute of Applied Ecology provided logistical and
that natural recolonization of the study area is possible. technical support for the analyses. R. A. Powell, R. Harris, and 2
In summary, habitat availability (as we defined it) will not be anonymous referees provided useful comments on an earlier version of
an issue for bear conservation in the foreseeable future on the the manuscript.
landscape scale. This indication is important in shaping and
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