Plant Ecol
DOI 10.1007/s11258-016-0589-6
Predictive modelling of climax oak trees in southern Spain:
insights in a scenario of global change
Javier López-Tirado . Pablo J. Hidalgo
Received: 2 October 2015 / Accepted: 13 March 2016
Ó Springer Science+Business Media Dordrecht 2016
Abstract Predictions of an increase in mean temperatures and a widespread reduction in annual rainfall
over the next few decades are consistent. Such drastic
changes can have a serious, irreversible impact on the
current distribution of trees and their ecosystems.
Oaks are the most frequent trees in the better preserved
areas of the Mediterranean basin; therefore, it is
essential to understand potential shifts in their distribution for proper management and protection. The
area studied in this work spans approximately 8.7
million hectares. The results obtained at 200 m
resolution were subjected to logistic regression over
four periods: current period (1961–2000), early
twenty-first century (2011–2040), middle twenty-first
century (2041–2070) and late twenty-first century
(2071–2100). These periods were examined by using
the CNCM3 model in an A1b scenario at 200 m
resolution for the study area. Four of the five target
species exhibited a narrower potential distribution in
the twenty-first century. Cork oak and gall oak
underwent a drastic potential reduction; on the other
Communicated by Joseph Paul Messina.
J. López-Tirado (&) P. J. Hidalgo
Department of Environmental Biology and Public Health,
Faculty of Experimental Sciences, International Campus
of Excellence of the Sea (CEIMAR), International
Campus of Excellence for Environment, Biodiversity and
Global Change (CEICAMBIO), University of Huelva,
Avda. Tres de Marzo s/n, 21071 Huelva, Spain
e-mail: [email protected]
hand, Pyrenean oak and Algerian oak might find
shelter at higher elevations. By exception, holm oak
exhibited the opposite trend and was favoured by
projected global warming. This projection is rather
adverse for biodiversity and oak-dependent ecosystems. This study allowed an accurate prediction of the
potential distribution of five different oak species and
is therefore a promising, potentially effective tool for
developing high-resolution reforestation programmes.
Keywords Mediterranean oak trees Spatial
modelling Global change Southern Spain Reforestation programmes
Introduction
Tree species may undergo shifts in distribution within
a short time by the effect of global change. This is
especially important for species growing at the
boundaries of their current distribution. The Mediterranean basin is expected to be among the most stressed
areas in the future and Spain is especially suitable for
exploring global change scenarios (Márquez et al.
2011). Uniquely affected are the semidesert to hyperhumid areas and high mountains in southern Spain.
Increasing atmospheric CO2 levels have been associated with rises in temperatures and extreme variations
in rainfall patterns (Giorgi and Lionello 2008). These
changes can have severe consequences on species with
123
Plant Ecol
a highly specific ecology and a poor gene pool, which
will probably undergo mass extinction (Dirzo and
Raven 2003; Leakey and Levin 1996; Thuiller et al.
2011). By contrast, species with a high phenotypic
plasticity may be able to adapt to the changes (Nicotra
et al. 2010).
The genus Quercus comprises 531 species across
the world (Govaerts and Frodin 1998). Oaks are
widely represented in the northern hemisphere, where
they constitute one of the most important groups in
terms of ecology, biodiversity and economy (Axelrod
1983; Cañellas et al. 2006, 2007; Menitsky 2005;
Nixon 2006). North America has a large number of
Quercus species and Mexico hosts 160 species. China
is another centre of oak diversity, with more than a
hundred species (Hogan 2012). By contrast, about 30
grow in Europe, mostly in the Mediterranean region
(Govaerts and Frodin 1998; Simeone et al. 2013).
Holm oak [Quercus ilex subsp. ballota (Desf.) Samp]
and cork oak (Q. suber L.) are the two most frequent
evergreens in southern Spain, followed by deciduous
oaks such as gall oak (Q. faginea Lam.), Pyrenean oak
(Q. pyrenaica Willd.) and Algerian oak (Q. canariensis Willd.). All are climax species, i.e. species which
reach the late-successional step in the forest, remaining unchanged while the habitat is not disturbed.
Humans have transformed natural landscapes for
centuries. In fact, only a few isolated primary forests
remain. Oak forests are deemed ‘‘pure’’ when the
dominant species accounts for at least 90 % of all trees
(Madrigal 1994) and ‘‘mixed’’ otherwise. A traditional
management system for oak forests in wide use in
Spain (Vericat et al. 2012) involves establishing
wooded pasturelands, locally known as dehesas, by
removing the shrub layer to facilitate grass growth and
reduce tree density to 30–50 trees per hectare (Alejano
et al. 2011).
Although holm oak and cork oak forests are
estimated to have spanned up to 30 million hectares
in the past, their current distribution is limited to about
2–3 million hectares (Costa Tenorio et al. 2005).
According to Vericat et al. (2012), Mediterranean oak
formations presently occupy around 7.3 million
hectares. Evergreen oak forests are subjected to
special conditions including (a) moderate cold in
winter, (b) uneven rainfall during the year by effect of
the prevailing Mediterranean climate, (c) high temperatures in summer (the drought period) and (d) scant
soil nutrients (Costa Tenorio et al. 2005).
123
Spatial distribution model (SDM) relates the
observed distribution of a species with its environmental preferences in order to predict an overall
potential distribution (Olden et al. 2002; Guisan and
Thuiller 2005; Araùjo and Guisan 2006; Elith et al.
2006; Mellert et al. 2011). Thus, the fitness of the
species can be mapped at a given site in terms of
previously defined parameters (Mateo et al. 2011).
Modelling techniques for species distribution have
grown in use recently and have become useful tools for
ecosystem management and conservation (Hidalgo
et al. 2008; Vessella and Schirone 2013; López-Tirado
and Hidalgo 2014). SDM predictions are best when
made from large occurrence datasets—otherwise,
statistically supported models are virtually impossible
to compute (Felicı́simo et al. 2002).
Logistic regression (LR) is a probabilistic statistical
modelling technique used to relate a categorical
dependent variable to one or more independent
variables. LR can be applied to a whole territory by
virtue of its relating explanatory variables (Sumarga
2011). The dependent variable can be binary (BLR) or
multinomial (MLR). The latter has been used to model
spatial vegetation dynamics (Augustin et al. 2001;
Finley et al. 2009).
Conservation and economic purposes encouraged
us to study oak trees from southern Spain as these
forests are the basis of many activities in the
Mediterranean basin. This work goes beyond that of
López-Tirado and Hidalgo (2014), which only
addresses high-mountain conifers. These species are
clearly separated in their distributions with respect to
oaks, as the ecological requirements are markedly
different. High-mountain conifers inhabit mid-high
elevations especially in the eastern part of the study
area, whereas oaks mainly grow in mid-low elevation
in the western part. In this paper, a group of the
westernmost Mediterranean oak trees are modelled at
high resolution, including the study of the impact in a
context of global change. It is, therefore, a new and
powerful tool to understand, follow and manage these
economically interesting areas. Thus, management
and reforestation programmes can be carried out in
benefit of ecology/biodiversity and economy in the
forthcoming decades.
This work was primarily undertaken to (a) obtain a
high-resolution (200 m) potential distribution map for
five oak species in the south of Spain from LR models
and to (b) forecast the species suitability areas
Plant Ecol
throughout the twenty-first century in order to evaluate
the effects of global change on species bioclimatic
range dynamic.
desert areas in the southeast, and heavy rain areas in
the southwest.
Target species
Materials and methods
Study area
The study area is located in Andalusia, a region in
southern Spain spanning an area of approximately 8.7
million hectares (Fig. 1). The region encompasses two
mountain ranges: Sierra Morena and the Baetic range.
The former occupies a belt along the north and has
largely acid soils, its highest summits lying about 1300
m above sea level (a. s. l.). The latter is a limestone
formation located in the east of the country with its
highest point in the Iberian peninsula (3479 m a. s. l.).
Between them lies the Guadalquivir depression, used
for agriculture. The region varies widely in rainfall
pattern due to its topography. Thus, there are sub-
The range of Q. ilex L. spans longitudinally from
Portugal to Syria and latitudinally from Morocco–
Algeria to France (Debazac 1983). Holm oak constitutes a versatile and especially competitive species.
Quercus ilex subsp. ballota is the most restricted
taxon; it is specifically Mediterranean and grows
mainly in the Iberian peninsula and northern Africa.
This species is well adapted to the typical summer
droughts of the Mediterranean region and to growing
in areas with low annual precipitation (Herrera 2004;
Muñoz Álvarez 2010).
Cork oak occurs in the western Mediterranean
Basin, from Portugal to Italy longitudinally and from
North Africa to France latitudinally. Quercus suber
has a low resistance to cold temperatures (Garcı́aMozo et al. 2001) and a preference for acid soils
Fig. 1 Location of the study area
123
Plant Ecol
(Montero 1988; Montoya 1980). Cork oaks are more
competitive than holm oaks in areas under oceanic
influence and on acid substrates. Long-term human
impacts in southwestern Spain have led to many mixed
sclerophyllous oak forests being displaced by pure
holm oak stands (Costa Tenorio et al. 2005).
Gall oaks exhibit various taxa as a result of their
polymorphism. The lack of distinct occurrence data
for Q. faginea subsp. faginea, subsp. alpestris (Boiss.)
Maire and subsp. broteroi (Cout.) A. Camus led us to
deal with the taxon sensu lato. Consistent with their
moisture requirements, gall oaks usually occur in
wetter areas than other widespread trees such as holm
and cork oaks.
The Pyrenean oak is a typically Iberian species that
grows from the west of France to the north of Morocco
(Ruiz de la Torre 2006; Carvalho 2005; Costa Tenorio
et al. 2005). In our study area, this species is found at
local sites with increased annual rainfall at highest
elevations on north-facing slopes. Its main stands are
found in the Baetic range. This tree has more stringent
water and altitudinal requirements than the previously
described oaks.
The Algerian oak, which belongs to the subgenus
Quercus (Do Amaral Franco 1990), is endemic of the
Ibero–Magrebian region (Portugal, Spain, Morocco,
Algeria and Tunisia). Its natural distribution in the
Iberian peninsula is quite disjunct, and its main stand
is in a subtropical vestigial stronghold in the southernmost area; however, isolated specimens can be
found inland in Sierra Morena. The Algerian oak is the
most water-demanding species of all studied here.
Dataset
Four different climatic periods were studied:
1961–2000 (current period), 2011–2040 (early
twenty-first century), 2041–2070 (middle twenty-first
century) and 2071–2100 (late twenty-first century).
Forecasting models were constructed in accordance
with IPCC’s 4th Assessment Report (AR) and model
CNCM3 was used in an A1b scenario (IPCC 2007).
The latter describes a world with a global population
that peaks in mid-century and a balance across all
sources. The intermediate characteristics among scenarios were the key to choose it (B1 and A2 scenarios
are the most extreme). Developing accurate highresolution models entails using well-known, validated
data. Unfortunately, IPCC’s 5th AR at 200 m
123
resolution for the study area is still under construction.
The pedological variable was assumed to remain
constant throughout (Bertrand et al. 2012).
Raw environmental data were obtained from the
Environmental Information Network of Andalusia
(REDIAM). Tree species distribution data were
retrieved from the regional Environmental and Water
Agency. Raw pedological data were obtained from the
Map of Soil pH in Europe (Land Resources Management Unit, Institute for Environment and Sustainability, European Commission, http://eusoils.jrc.ec.
europa.eu/library/data/ph/, last accessed October
2014). A grid of approximately 2 million points was
constructed by means of ArcGIS 10 (ESRI, 2010).
Flooded areas under the influence of tides on the
coastline (marshes and rice crops) were excluded.
Various combinations of climatic and bioclimatic
variables were tested in order to identify the most
accurate model. The use of few variables (around 6–7)
resulted in a low value of Nagelkerke’s R2. Thus, the
number of variables was increased until 13 leading to
the dataset shown in Table 1. CI (continentality index),
AAI (annual aridity index) and OMB (ombrotype)
were calculated in accordance with the Worldwide
Bioclimatic Classification System of the Phytosociological Research Center (www.globalbioclimatics.org,
last accessed November 2014). Two species (holm oak
and gall oak) have no definite preference with regard to
pH, whereas the other three tend to grow on acidic
soils; by exception, the Pyrenean oak can grow even at
neutral pH (Costa Tenorio et al. 2005).
In order to increase the presence of original species
growing in the Guadalquivir depression, area used for
agriculture in the main, around 660,000 hectares
(roughly 8.5 % of the study area), was digitized with
Google Earth software. To simplify the identification
of the species, those polygons near roads were
identified by means of the Street View tool in Google
Earth. The remaining polygons were confirmed via
field work. The main species was holm oak, followed
by wild olive (Olea europaea L. subsp. europaea var.
sylvestris (Mill.) Lehr) and cork oak.
Development and assessment of models
The shapefile (DBF file) constructed by means of
ArcGIS 10 (ESRI, 2010) was used with the software
SPSS Statistics 20 (IBM, 2011). Application of MLR
analysis to the five target species revealed large
Plant Ecol
Table 1 Explanatory variables used as predictors
Variable
Unit
Abbrev.
Source
Environmental
Spring rainfall
mm
SPR
Monthly rainfall
Summer rainfall
Autumn rainfall
mm
mm
SUR
AUR
Monthly rainfall
Monthly rainfall
Winter rainfall
mm
WIR
Monthly rainfall
Spring mean temperature
°C
SPT
Monthly mean temperature
Summer mean temperature
°C
SUT
Monthly mean temperature
Autumn mean temperature
°C
AUT
Monthly mean temperature
Monthly mean temperature
Winter mean temperature
°C
WIT
Reference evapotranspiration
mm/year
RET
Monthly potential evapotranspiration
Continentality index
°C
CI
Monthly mean temperature
Annual aridity index
–
AAI
Annual potential evapotranspiration and annual rainfall
Ombrotype
–
OMB
Annual rainfall and monthly mean temperature
–
pH
Soil pH in Europe
Pedological
pH value
Units, abbreviations used in the text and source
differences in number of records at 200 m resolution.
Holm oak was the most frequent species with 141,999
occurrence points, followed by cork oak (80,871
points) and gall oak (27,711 points). Pyrenean oak and
Algerian oak were the two lesser represented species
with 1949 and 1706 occurrence points, respectively.
This led us to model Pyrenean oak and Algerian oak
separately by BLR, using the forward conditional
method. MLR was applied with the ‘‘main effects’’
method, which is insensitive to small data changes and
provides accurate predictions (Augustin et al. 2001).
Once the models were run, the sign of the values in
column b indicated whether a given variable supported (?) or avoided (-) the presence of the species
concerned. Logistic formulae were applied to the
whole territory, each point on the grid having a
probability value ranging from 0 (impossible presence) to 1 (most likely presence).
Models were adjusted via Nagelkerke’s R2
(Nagelkerke 1991), with values higher than 0.4
indicating good calibration (Bässler et al. 2010). The
Akaike Information Criterion (AIC; Akaike 1973) and
the Bayesian Information Criterion (BIC; Schwarz
1978) were used to validate the MLR according to
Burnham and Anderson (2004). Calculations for BLR
included area under the curve (AUC) on account of its
power for measuring the quality of a probabilistic
classifier (Fawcett 2006; Fielding and Bell 1997; BenDavid 2008; Vuk and Curk 2006).
Results
Table 2 shows the mean (standard deviation) and
extreme values of all explanatory variables used for
each species.
Overall, seasonal rainfall (SPR, SUR, AUR and
WIR) peaked for holm oak as a result of its vast
representation in the study area. By contrast, the
minimum values of SPR and SUR were highest for
Pyrenean oak. The fact that the distribution of
Algerian oak is concentrated in an area with the
highest rainfall led to the highest mean SPR, AUR and
WIR values. Gall oak, Pyrenean oak and holm oak had
the highest mean values in summer (SUR) by effect of
their reaching higher elevations than the other species.
Gall oak had mean AUR and WIR values in between
those for the other species, but quite higher SPR
values.
Seasonal temperatures clearly reflected the lowest
mean values for Pyrenean oak as a result of its stands
being at the highest mean elevations. This was also the
case with gall oak, which came after Pyrenean oak in
this respect. Also worth special note here is the fact
123
6.08 (0.78)
Maximum, minimum, mean and standard deviation (S.D.). For abbreviations, see Table 1
9.80
6.94
5.37
5.70 (0.26)
7.50
4.63
6.80 (0.89)
7.66
4.67
6.57 (0.74)
7.48
4.37
5.38 (0.52)
7.71
1.83
pH
4.38
2.70
5.70 (1.41)
8.20
2.40
4.83 (0.86)
13.80
1.50
4.40 (1.51)
10.80
1.70
3.93 (1.19)
13.70
3.23 (1.19)
OMB
1.00
18.90
11.40
0.38
0.79 (0.20)
14.46 (1.16)
20.00
1.81
0.62
13.70
16.35 (1.81)
1.27 (0.20)
3.08
20.70
11.40
0.26
1.30 (0.33)
18.00 (1.43)
20.40
2.78
0.35
10.50
15.87 (1.72)
1.30 (0.37)
4.27
20.80
1.74 (0.45)
AAI
0.23
17.76 (1.34)
CI
10.80
12.90
1051.00
549.00
849.80 (84.67)
1080.00
645.00
939.88 (77.32)
1197.00
371.00
930.04 (82.32)
1171.00
493.00
955.20 (92.57)
1225.00
994.89 (80.85)
RET
421.00
19.13
14.33
6.93
10.35 (1.00)
17.49 (0.78)
17.86
10.96
2.63
9.53
13.67 (1.54)
6.45 (1.46)
12.30
19.76
9.63
2.13
7.19 (1.72)
15.23 (1.82)
20.46
13.40
5.50
12.40
17.71 (1.34)
10.02 (1.48)
13.20
20.50
8.56 (1.75)
WIT
1.20
16.74 (1.89)
AUT
8.76
16.26
25.03
20.46
23.08 (0.70)
24.80
17.20
20.91 (1.70)
26.66
17.23
23.07 (1.77)
26.70
18.80
24.02 (1.17)
27.33
24.25 (1.72)
SUT
16.90
851.00
217.00
11.16
14.34 (0.83)
519.82 (117.79)
590.00
15.36
6.80
204.00
307.71 (56.26)
10.88 (1.52)
17.03
1040.00
92.00
5.83
12.39 (1.87)
309.72 (91.34)
937.00
17.53
9.33
120.00
345.13 (108.12)
14.67 (1.28)
17.63
1041.00
13.95 (1.90)
SPT
5.46
244.82 (86.00)
WIR
54.00
48.00
504.00
162.00
318.04 (69.40)
369.00
143.00
216.36 (35.57)
529.00
87.00
200.52 (46.42)
503.00
99.00
226.87 (54.50)
529.00
174.82 (43.87)
AUR
61.00
403.00
108.00
12.00
29.97 (6.71)
251.74 (55.33)
308.00
62.00
18.00
126.00
193.61 (29.58)
36.12 (9.03)
92.00
488.00
73.00
6.00
42.11 (13.43)
200.56 (55.26)
446.00
61.00
8.00
91.00
182.13 (45.85)
30.62 (9.36)
92.00
489.00
61.00
35.51 (11.48)
12.00
155.60 (44.77)
SUR
Min.
123
SPR
Mean (S.D.)
Mean (S.D.)
Mean (S.D.)
Min.
Mean (S.D.)
Mean (S.D.)
Max.
Q. suber
Q. ilex subsp. Ballota
Variable
Table 2 Summary of explanatory variables for each species
Max.
Q. faginea
Min.
Max.
Q. pyrenaica
Min.
Max.
Q. canariensis
Min.
Max.
Plant Ecol
that the mean SUT and WIT values for Algerian oak
(viz., the third species with the coolest temperature in
summer and that with the warmest in winter) were
consistent with its distribution in an oceanic environment where temperatures remain more balanced
during the year. Holm oak was the individual species
exhibiting the warmest temperatures in SUT, and also
the most thermic species in this study, even though it
exhibited the widest range of values for these
variables.
The mean reference evapotranspiration (RET) was
lower for deciduous species (Pyrenean oak, gall oak
and Algerian oak) than for the evergreens (holm oak
and cork oak). The lowest value of the continentality
index (CI) was for the main stand of Algerian oak near
the coastline, followed by cork oak. A more inland
distribution of Pyrenean oak, holm oak and gall oak
resulted in increased values of this variable. Algerian
oak was the species with the lowest AAI by effect of
the large amount of rainfall in its current distribution.
Pyrenean oak, cork oak and gall oak were very similar
to one another in this respect and had AAI values in
between those for the other species. On the other hand,
holm oak was the most arid. This species exhibited the
lowest OMB values and Algerian oak the highest.
Finally, the pedological variable (pH) was always
less than 7.00. The lowest values were those for cork
oak and Algerian oak, two species with a preference
for acid soils and lithology. By contrast, Pyrenean oak,
which also grows on acid soils, has pH \ 5 in Sierra
Morena even though it also occupies siliceous terrains
with neutral pH in the stands of Sierra Nevada
National Park. Around 70 % of the grid points
containing Pyrenean oaks are in the latter area—
hence their high mean pH values, which, however, are
lower than those for Algerian oak and cork oak in
Sierra Morena stands. The other species (holm oak and
gall oak) are indifferent to soil pH and exhibit a mean
value of 6.08 and 6.57, respectively.
Table 3 shows the distribution of the explanatory
variables among the four periods. The forecasted
scenarios for the twenty-first century show a general
decrease in seasonal rainfall, particularly in the last
period, except for SUR. An overall increase in
seasonal temperatures is forecasted and so is one in
RET, CI and AAI; on the other hand, a general
decrease in OMB is expected. Minimum and maximum values are expected to change in accordance with
an east-to-west gradient. Table 4 shows the output
8.20
c.t.
c.t.
c.t.
c.t.
c.t.
c.t.
7.90
0.32
0.40
2.04 (0.77)
2.56 (0.80)
0.40
2.23 (0.88)
11.94
0.30
2.44 (0.86)
8.95
c.t.
c.t.
c.t.
4.33
6.40 (0.84)
pH
7.91
13.20
7.51
0.23
0.50
2.82 (1.18)
2.03 (0.77)
0.50
3.11 (1.37)
OMB
14.20
0.21
1.88 (0.76)
AAI
7.37
20.90
10.00
17.07 (1.84)
10.50
17.02 (1.81)
CI
20.80
371.00
Models performance
pH was assumed to remain constant in time (c.t.). For abbreviations, see Table 1
1490.00
22.20
10.40
18.08 (2.03)
9.99
17.35 (1.93)
21.30
15.36
1.26
397.00
1134.81 (107.17)
10.98 (2.04)
399.00
1434.00
0.96
1347.00
1098.29 (101.72)
14.33
394.00
1039.54 (94.40)
-1.60
8.93 (2.13)
975.54 (87.31)
WIT
9.69 (2.07)
scores provided by the logistic regression models.
Nearly all species allowed estimation of the thirteen
explanatory variables—by exception, Algerian oak
dismissed RET.
Nagelkerke’s R2 score was 0.779 with MLR, and
0.683 for Pyrenean oak and 0.722 for Algerian oak
with BLR. AIC and BIC as determined with the MLR
method exhibited lower values with the full or final
model than with the null or intercept only model.
Finally, the BLR calculated values of AUC were 0.969
for Pyrenean oak and 0.977 for Algerian oak.
Figure 2 shows the current and potential distribution of the target species in the four time periods. Holm
oak, cork oak and gall oak possess a wider distribution
at present than Pyrenean and Algerian oaks. All
species could be more widely distributed in the current
period than they are today (particularly holm oak and
Pyrenean oak). However, the three scenarios in the
twenty-first century exhibit a considerable decrease in
the potential for all species except holm oak, which
will seemingly be favoured by the forecasted changes.
Discussion
RET
1255.00
13.93
-0.40
10.59 (2.02)
15.01
23.83
31.66
18.26
8.33
20.35 (2.20)
28.09 (1.84)
7.33
22.83
16.46
19.38 (2.16)
21.70
18.03 (2.16)
25.09 (1.88)
27.66
5.93
4.36
16.85 (2.18)
AUT
13.93
20.66
12.33
23.96 (1.91)
SUT
28.56
26.76 (1.85)
30.33
891.00
20.73
5.60
17.11 (2.06)
4.76
18.80
15.08 (2.12)
2.90
1.36
14.00 (2.18)
SPT
19.10
16.46 (2.05)
20.03
245.00
15.00
22.00
206.79 (92.24)
84.23 (27.01)
27.00
764.00
32.00
1144.00
192.82 (78.54)
108.93 (38.40)
254.31 (106.08)
1048.00
532.00
24.00
22.00
238.28 (98.38)
WIR
20.00
46.00
169.08 (52.22)
AUR
378.00
130.87 (37.52)
367.00
156.00
339.00
24.00
2.00
32.16 (13.08)
100.66 (33.87)
423.00
95.00
29.00
2.00
135.80 (42.80)
31.19 (12.31)
32.95 (13.40)
122.90 (42.06)
493.00
92.00
25.00
30.88 (11.93)
SUR
5.00
34.00
2.00
147.91 (47.92)
SPR
406.00
Max.
Max.
Max.
Min.
Mean (SD)
Min.
Mean (SD)
155.00
Mean (S.D.)
Min.
Mean (SD)
Min.
Late twenty-first century
Middle twenty-first century
Early twenty-first century
Current period
Variable
Table 3 Mean, minimum and maximum values of the climatic and bioclimatic variables for the study area in the four time periods
Max.
Plant Ecol
Using Nagelkerke’s R2 score for MLR (holm oak, cork
oak and gall oak) and BLR (Pyrenean oak and
Algerian oak) ensured an excellent fit of the models
for all target species. The AIC and BIC scores
obtained with MLR confirm a good validation of the
full model. Also, the AUC scores for Pyrenean oak and
Algerian oak with BLR were near-unity and hence
extremely accurate according to Swets (1988). Models
were constructed from 13 explanatory variables
obtained from a long-term data series, which made
the resulting potential distribution maps robust and
accurate. The variable pH was also important for the
target species (especially for those preferring a
specific pH range) (Bertrand et al. 2012).
Interpretation of b values
Among climatic and bioclimatic variables, seasonal
rainfalls (SPR, SUR, AUR and WIR) were highly
unbalanced between summer and the other seasons by
effect of summer being the drought period under a
Mediterranean climate. Only SUR can be expected to
123
123
0.071
-
-2.646 \0.0005
12.252 \0.0005
pH
Intercept/Constant
un. unquantifiable
0.202
-1.602 \0.0005
OMB
0.008 \0.0005
0.096
0.007
1.008
-5.019 \0.0005
WIT
RET
0.189
2.724
1.002 \0.0005
AUT
-2.345 \0.0005
9.545
2.256 \0.0005
SUT
-1.663 \0.0005
3.276
1.187 \0.0005
SPT
CI
1.026
1.002
0.026 \0.0005
0.002 \0.0005
AUR
WIR
AAI
1.008
0.981
0.008 \0.0005
-0.019 \0.0005
SPR
36.614 \0.0005
-4.295 \0.0005
-3.297 \0.0005
-4.884 \0.0005
-3.176 \0.0005
0.011 \0.0005
-5.342 \0.0005
0.176 \0.0005
3.153 \0.0005
1.063 \0.0005
0.038 \0.0005
0.008 \0.0005
-0.116 \0.0005
0.020 \0.0005
Sig.
b
Exp(b)
b
Sig.
Quercus suber
Quercus ilex subsp. ballota
SUR
Variable
-
0.014
0.037
0.008
0.042
1.011
0.005
1.193
23.416
2.895
1.038
1.008
0.890
1.020
Exp(b)
0.021 \0.0005
Sig.
11.458 \0.0005
-2.091 \0.0005
-1.889 \0.0005
-6.637 \0.0005
-1.317 \0.0005
0.009 \0.0005
-2.688 \0.0005
-0.748 \0.0005
2.496 \0.0005
0.550 \0.0005
0.015 \0.0005
-0.002 \0.0005
-0.074 \0.0005
b
Quercus faginea
-
0.124
0.151
0.001
0.268
1.009
0.068
0.473
12.133
1.732
1.015
0.998
0.929
1.021
Exp(b)
0.073 \0.0005
Sig.
0.011
53.377 \0.0005
-0.315 \0.0005
-6.102 \0.0005
-13.248 \0.0005
-5.126 \0.0005
0.026 \0.0005
-3.423 \0.0005
-4.912 \0.0005
6.312 \0.0005
-0.449
0.055 \0.0005
-0.016 \0.0005
-0.100 \0.0005
b
Quercus pyrenaica
-
0.730
0.002
un.
0.006
1.026
0.033
0.007
551.203
0.638
1.057
0.984
0.905
1.076
Exp(b)
0.056 \0.0005
Sig.
-
0.002
26.419 \0.0005
0.350
-7.870 \0.0005
-4.212 \0.0005
2.646 \0.0005
-
2.396 \0.0005
2.315 \0.0005
-3.680 \0.0005
-3.186 \0.0005
0.053 \0.0005
0.038 \0.0005
-0.096 \0.0005
b
Quercus canariensis
Table 4 Output scores of the explanatory variables in the equation table for each species: regression coefficient (b), significance (Sig.) and odds ratio [Exp(b)]
-
1.419
un.
0.015
14.104
-
10.982
10.127
0.025
0.041
1.054
1.038
0.908
1.057
Exp(b)
Plant Ecol
Plant Ecol
slightly increase along the twenty-first century, possibly as a result of more frequent storm events leading
to pouring rain and increased land erosion. SPR and
AUR b values were positive, and SUR values negative, for all species. On the other hand, WIR b values
were positive or negative depending on the particular
species. Thus, the target species tend to prefer those
areas in which rainfall is higher in spring and autumn,
and to avoid those where rainfall is lower in summer.
Gall oak and Pyrenean oak also tend to evade the
decreased rainfall of winter. In any case, seasonal
rainfall values were always near-zero, which is
suggestive of a weak relationship—especially for
holm oak. This species adapts better than the others to
the harsh xeric conditions of the Mediterranean
climate (Costa Tenorio et al. 2005; López González
2007; Muñoz Álvarez 2010).
Regarding seasonal temperatures, b values differed
in sign and some absolute values were farther from zero
than others. In fact, temperatures proved influential on
the target species (particularly SUT and WIT in the most
extreme seasons). Also, all species except Algerian oak
had positive SUT values and negative WIT values. This
suggests that they are comfortable with the summer
mean temperatures but not with the winter temperatures.
By contrast, Algerian oak, which is the only species
growing in vestigial subtropical environments, exhibited inverted signs; thus, it avoids temperature rises in
summer. The mean seasonal temperatures for cork oak
are consistent with actual growth in warm areas, and also
with its avoiding higher, colder areas—which are more
suitable for Pyrenean oak. The variable RET, which is
influenced by a number of climatological conditions
such as solar radiation, air temperature, air humidity and
wind speed (El-Shafie et al. 2014), exhibited positive,
near-zero values for all species except Algerian oak,
which was excluded from the model. Deciduous species
tend to undergo less marked evaporation and transpiration, and hence, the lower mean RET values are shown
for them.
Again, Algerian oak exhibited a different trend in
CI, with positive rather than negative b values as in the
other species. This result is suggestive of settlement of
Algerian oaks in inland territories, consistent with the
small populations growing in Sierra Morena (Muñoz
Álvarez 2010).
The explanatory variables AAI and OMB, which
measure similar arid–humid environmental conditions, exhibited negative b values for all species. This
suggests avoidance of aridity and humid–subhumid
ombrotypes, respectively, a controversial aspect
which might be misinterpreted unless the two variables are dealt with together. Oak stands spread across
an intermediate aridity–humidity range, and the study
area includes the most arid–humid zones in the Iberian
Peninsula. As a result, oaks are not adapted to the
extremely arid conditions in the southeast, nor to the
most humid ombrotypes and highest rainfall. Arid
zones are occupied by edaphoxerophilous formations
where the presence of trees is a token. On the other
hand, humid zones are occupied by high-mountain
vegetation dominated by pulviniform shrubs. Conifers
also grow at extreme elevations under hyperhumid,
humid and subhumid ombrotypes (Valle 2004; LópezTirado and Hidalgo 2014). Holm oak, cork oak, gall
oak and Pyrenean oak exhibited negative AAI values
that were farther from zero than their OMB values (i.e.
they were more markedly affected by a lack of aridity
than of humidity). This is especially true for holm oak,
but not for gall oak or Pyrenean oak, which have more
stringent water requirements. On the other hand,
Algerian oak had OMB values farther from zero than
its AAI values, the two being similar in any case.
Finally, pH had negative b values for all species
except Algerian oak, which had positive, near-zero
values. None of the target species is exclusive of basic
soils; rather, some are indifferent and others prefer
acid soils. Cork oak exhibited the strongest preference
for acid soils (Montero 1988; Montoya 1980), consistent with its current distribution in the southwest of the
Iberian peninsula.
Potential distribution along the twenty-first
century
The expected increase in temperature and decrease in
annual rainfall in southern Europe (Giorgi and
Lionello 2008; IPCC 2007; Morales et al. 2005;
Schröter et al. 2005) are captured by the data presented
in Table 3. According to Bussotti et al. (2014), plants
growing in the Mediterranean basin are currently near
the optimum temperature. As a consequence, the
potential distribution of four of our target species in
future periods exhibits a general decrease. Cork oak,
gall oak and Algerian oak are expected to undergo a
widespread reduction in distribution in the study area;
on the other hand, Pyrenean oak may be able to find
new areas suitable for growth. The latter could also
123
Plant Ecol
Fig. 2 Current (green colour) and potential distribution of
holm oak, cork oak, gall oak, Pyrenean oak and Algerian oak in
the study area in the four studied periods as determined with a
logistic regression model. In the potential distribution, shades of
grey represent frequency, which ranges from black (definite
presence) to white (definite absence). (Color figure online)
undergo an upslope migration potentially leading to a
double-sided trend according to the location of the
populations. The current scattered stands of Pyrenean
oak in Sierra Morena almost reach the summit, so they
might be lost if the current environmental conditions
change as predicted. However, an upslope migration
of the treeline in the Baetic range is possible. Galiano
et al. (2010) studied the behaviour of conifers and oaks
in the Pyrenees by using general and generalized linear
models. They predicted an upward migration of oaks
(Q. ilex L. and Q. humilis Lam.) towards the current
distribution of Scots pine. The results in our study area
are thus consistent with theirs since Pyrenean oak
could also grow higher and occupy part of the potential
distribution of Scots pine in Sierra Nevada National
Park (López-Tirado and Hidalgo 2014). As suggested
by Bede-Fazekas et al. (2014) for various Mediterranean pines, other species could also follow a
latitudinal and altitudinal upward migration. Northwestern Africa is expected to become unsuitable for
pines, and the trees are expected to migrate to the north
as a result. A global movement is thus expected by
which oaks and Mediterranean pines could partially
occupy the current distribution of high-mountain
conifers (Garamvoelgyi and Hufnagel 2013; Garcı́aValdés et al. 2013; Ruiz-Labourdette et al. 2013;
Vessella et al. 2015).
Plant phenology in the Mediterranean region has
been altered by global change (Gordo and Sanz 2010).
Some holm oak specimens have blossomed in December 2015 in the study area, which normally occurs
from March to May (pers. obs.). Therefore, plant
species can adjust to the novel conditions (Nicotra
et al. 2010). The areas lost by the rest of the species
might be occupied by holm oaks, which possess a very
high ecological plasticity. In fact, Q. ilex L. is known
to have a high drought tolerance allowing it to easily
adapt to variability in precipitation (Ferrio et al. 2003;
Martı́nez-Ferri et al. 2000; Methy et al. 1997). Holm
oak is a typical inland species capable of growing in
harsher continental conditions; also, it is expected to
benefit from the increase in AAI during the twentyfirst century (see Table 3). As a result, this species
might expand its current inland distribution by
spreading widely across the study area and being
more competitive in those sites closer to the coastline.
123
Plant Ecol
Conclusions
The forecasted potential distribution for the
1961–2000 period is wider than the current distribution as a result of the impact of human actions for
centuries (Devy-Vareta 1985; Garcia 1986; Daveau
1988). The twenty-first century might witness an
overall decrease in potential distribution of the target
species—holm oak excepted—in response to expected
global change. This is especially important at a time
where habitat fragmentation is exacerbating the
adverse impact of global change (Matesanz and
Valladares 2014). These results are also especially
interesting for rare species (Fois et al. 2015) such as
Pyrenean oak, which might find shelter at the highest
elevations like other tree species in Europe (BedeFazekas et al. 2014; Bertrand et al. 2012; Parmesan
and Yohen 2003; Rabasa et al. 2013). In addition, we
are increasingly understanding the effect of global
change on the potential distribution of current stands
and the survival potential of reforested areas.
In conclusion, LR models provided useful current
and forecasted potential distribution maps with a high
level of accuracy. The predictions obtained, based on
high-resolution data (200 m), should be useful to
design reforestation, management and conservation
programmes. Also, they might be useful to address
socioeconomic issues of cork management in cork oak
dehesas among others (Bugalho et al. 2011).
Acknowledgments The authors are grateful to the Economy,
Innovation, Science and Employment Council of the Andalusian
Regional Government for supporting this research in the
framework of the Project ‘‘Modelo espacial de distribución de
las quercı́neas y otras formaciones forestales de Andalucı́a: una
herramienta para la gestión y la conservación del patrimonio
natural’’ (Code P10-RNM-6013). This is the contribution n8 118
from the CEIMAR Journal Series. We also thank the
International Campus of Excellence for Environment,
Biodiversity and Global Change (CEICAMBIO).
References
Akaike H (1973) Information Theory as an Extension of the
Maximum Likelihood Principle. Second international
symposium on information theory. Akademiai Kiado,
Budapest, pp 267–281
Alejano R, Vazquez-Pique J, Carevic F, Fernandez M (2011) Do
ecological and silvicultural factors influence acorn mass in
Holm-oak (southwestern Spain)? Agrofor Syst 83(1):
25–39
Araùjo MB, Guisan A (2006) Five (or so) challenges for species
distribution modeling. J Biogeogr 33:1677–1688
Augustin NH, Cummins RP, French DD (2001) Exploring
spatial vegetation dynamics using logistic regression and a
multinomial logit model. J Appl Ecol 38(5):991–1006
Axelrod DI (1983) Biogeography of oaks in the Arcto-Tertiary
province. Ann Mo Bot Gard 70:629–657
Bässler C, Müller J, Hothorn T, Kneib T, Badeck F, Dziock F
(2010) Estimation of the extinction risk for high-montane
species as a consequence of global warming and assessment of their suitability as cross-taxon indicators. Ecol
Indic 10:341–352. doi:10.1016/j.ecolind.2009.06.014
Bede-Fazekas A, Horvath L, Kocsis M (2014) Impact of climate
change on the potential distribution of Mediterranean
pines. Idojaras 118:41–52
Ben-David A (2008) About the relationship between ROC
curves and Cohen’s kappa. Eng Appl Artif Intel 21(6):
874–882
Bertrand R, Perez V, Gegout JC (2012) Disregarding the
edaphic dimension in species distribution models leads to
the omission of crucial spatial information under climate
change: the case of Quercus pubescens in France. Glob
Change Biol 18:2648–2660
Bugalho MN, Caldeira MC, Pereira JS, Aronson J, Pausas JG
(2011) Mediterranean cork-oak savannas require human
use to sustain biodiversity and ecosystem services. Front
Ecol Environ 9:278–286
Burnham KP, Anderson DR (2004) Multimodel inference understanding AIC and BIC in model selection. Sociol
Methods Res 33:261–304. doi:10.1177/00491241042
68644
Bussotti F, Ferrini F, Pollastrini M, Fini A (2014) The challenge
of Mediterranean sclerophyllous vegetation under climate
change: from acclimation to adaptation. Environ Exp Bot
103:80–98
Cañellas I, Roig S, Montero G (2006) Pruning influence on
acorn yield in cork-oak open woodland. International
congress on silvopastoralism and sustainable management:
silvopastoralism and sustainable land management. CABI
Publishing, Lugo, pp 110–111
Cañellas I, Roig S, Poblaciones MJ, Gea-Izquierdo G, Olea L
(2007) An approach to acorn production in Iberian dehesas.
Agrofor Syst 70:3–9
Carvalho JP (2005) O Carvalho Negral. Agro-UTAD, Vila-Real
Costa Tenorio M, Morla Juaristi C, Sainz Ollero H (2005) Los
Bosques Ibéricos. Una interpretación geobotánica. Editorial Planeta, Barcelona
Daveau S (1988) Notas e Recensões. Progressos recentes no
conhecimento da evolução holocénica da cobertura vegetal
em Portugal e nas regiões vizinhas (Recent progress in the
knowledge of the Holocene evolution of the forest cover in
Portugal and neighbour regions. Finisterra (Lisboa), XXIII
45:101–115
Debazac EF (1983) Temperate broad-leaved evergreen forest of
the Mediterranean region and Middle East. Ecosystems of
the world 10: temperate broad-leaved evergreen forests.
Elsevier, Amsterdam, pp 107–123
Devy-Vareta N (1985) Para uma geografia histórica da floresta
Portuguesa. As matas medievais e a ‘‘coutada velha’’ do rei
(For a historical geography of the Portuguese forest.
123
Plant Ecol
Medieval forests and king’s old hunting ground). Rev Fac
Let – Geog, Ser I, vol I, Porto, pp 47–67
Dirzo R, Raven PH (2003) Global state of biodiversity and loss.
Ann Rev Environ Resour 28:137–167
Do Amaral Franco J (1990) Quercus L. Flora Ibérica, vol II.
CSIC, Madrid, pp 15–36
Elith J, Graham CH, Anderson RP, Dudı́k M, Ferrier S, Guisan
A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A,
Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C,
Nakamura M, Nakazawa Y, Overton JM, Peterson AT,
Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire
RE, Soberon J, Williams S, Wisz MS, Zimmermann NE
(2006) Novel methods improve prediction of species’
distributions from occurrence data. Ecography 29:129–151
El-Shafie A, Najah A, Alsulami HM, Jahanbani H (2014)
Optimized neural network prediction model for potential
evapotranspiration utilizing ensemble procedure. Water
Resour Manag 28:947–967
Fawcett T (2006) An introduction to ROC analysis. Pattern
Recogn Lett 27(8):861–874
Felicı́simo AM, Francés E, Fernández JM, González-Dı́ez A,
Varas J (2002) Modeling the potential distribution of forests with a GIS. Photogramm Eng Rem S 68(5):455–461
Ferrio JP, Florit A, Vega A, Serrano L, Voltas J (2003) Delta c-13
and tree-ring width reflect different drought responses in
Quercus ilex and Pinus halepensis. Oecologia 137:512–
518
Fielding AH, Bell JF (1997) A review of methods for the
assessment of prediction errors in conservation presence/
absence models. Environ Conser 24(1):38–49
Finley AO, Banerjee S, McRoberts RE (2009) Hierarchical
spatial models for predicting tree species assemblages
across large domains. Ann Appl Stat 3:1052–1079. doi:10.
1214/09-aoas250
Fois M, Fenu G, Cuena Lombraña A, Cogoni D, Bacchetta G
(2015) A practical method to speed up the discovery of
unknown populations using Species Distribution Models.
J Nat Conserv 24:42–48
Galiano L, Martı́nez-Vilalta J, Lloret F (2010) Drought-induced
multifactor decline of Scots pine in the Pyrenees and
potential vegetation change by the expansion of co-occurring oak Species. Ecosystems 13:978–991
Garamvoelgyi A, Hufnagel L (2013) Impacts of climate change
on vegetation distribution no. 1 climate change induced
vegetation shifts in the palearctic region. Appl Ecol Environ Res 11:79–122
Garcia JCS (1986) O Espaço medieval da reconquista no
sudoeste da Penı́nsula Ibérica (The medieval space of the
Reconquest of the southwest of Iberian peninsula). Lisboa.
Centro de Estudos Geográficos. Col. Chorographia, Série
Histórica - Estudos e Documentos Comentados, no 2
Garcı́a-Mozo H, Hidalgo PJ, Galán C, Gómez-Casero MT,
Domı́nguez E (2001) Catkin frost damage in Mediterranean cork-oak (Q. suber L.). Isr J Plant Sci 49:41–47
Garcı́a-Valdés R, Zavala MA, Araújo MB, Purves DW (2013)
Chasing a moving target: projecting climate change-induced shifts in non-equilibrial tree species distributions.
J Ecol 101:441–453
Giorgi F, Lionello P (2008) Climate change projections for the
Mediterranean region. Glob Planet Change 63:90–104
123
Gordo O, Sanz JJ (2010) Impact of climate change on plant
phenology in Mediterranean ecosystems. Glob Change
Biol 16:1082–1106
Govaerts R, Frodin DG (1998) World Checklist and Bibliography of Fagales. Royal Botanic Gardens, Kew, p 408
Guisan A, Thuiller W (2005) Predicting species distribution:
offering more than simple habitat models. Ecol Lett
8:993–1009
Herrera CM (coord) (2004) El monte mediterráneo en Andalucı́a. Consejerı́a de Medio Ambiente, Junta de Andalucı́a,
Sevilla, Spain
Hidalgo PJ, Marin JM, Quijada J, Moreira JM (2008) A spatial
distribution model of cork-oak (Quercus suber) in southwestern Spain: a suitable tool for reforestation. For Ecol
Manag 255:25–34. doi:10.1016/j.foreco.2007.07.012
Hogan CM (2012) Oak, Encyclopedia of Earth. National
Council for Science and the Environment, Washington, DC
IPCC (2007) Summary for policymakers climate change 2007:
the physical science basis. In: Contribution of working
group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge
Leakey R, Lewin R (1996) The sixth extinction: patterns of life
and the future of humankind. Anchor, New York
López González GA (2007) Guı́a de los árboles y arbustos de la
Penı́nsula Ibérica y Baleares, 3a edición. Ediciones MundiPrensa, Madrid
López-Tirado J, Hidalgo PJ (2014) A high resolution predictive
model for relict trees in the Mediterranean-mountain forests (Pinus sylvestris L., P. nigra Arnold and Abies pinsapo
Boiss.) from the south of Spain: a reliable management tool
for reforestation. For Ecol Management 330:105–114.
doi:10.1016/j.foreco.2014.07.009
Madrigal A (1994) Ordenación de Montes Arbolados. ICONA,
Madrid
Márquez AL, Real R, Olivero J, Estrada A (2011) Combining
climate with other influential factors for modelling the
impact of climate change on species distribution. Clim
Change 108:135–157. doi:10.1007/s10584-010-0010-8
Martı́nez-Ferri E, Balaguer L, Valladares F, Chico JM, Manrique E (2000) Energy dissipation in drought-avoiding and
drought-tolerant tree species at midday during the
Mediterranean summer. Tree Physiol 20:131–138
Mateo RG, Felicisimo AM, Munoz J (2011) Species distributions models: a synthetic revision. Rev Chil Hist Nat
84:217–240
Matesanz S, Valladares F (2014) Ecological and evolutionary
responses of Mediterranean plants to global change.
Environ Exp Bot 103:53–67
Mellert KH, Fensterer V, Kuechenhoff H, Reger B, Koelling C,
Klemmt HJ, Ewald J (2011) Hypothesis-driven species
distribution models for tree species in the Bavarian Alps.
J Veg Sci 22:635–646. doi:10.1111/j.1654-1103.2011.
01274.x
Menitsky YL (2005) Oaks of Asia. Science Publishers, Enfield
Methy M, Gillon D, Houssard C (1997) Temperature-induced
changes of photosystem II activity in Quercus ilex and
Pinus halepensis. Can J For Res 27:31–38
Montero G (1988) Modelos para cuantificar la producción de
corcho en alcornocales (Quercus suber L.) en función de la
Plant Ecol
calidad de estación y de los tratamientos selvı́colas. Dissertation, INIA, Madrid, Spain
Montoya JM (1980) Las áreas potenciales, reales y óptimas de
Quercus suber L. en España. Convención Mundial del
Corcho. Servicio de Publicaciones Agrarias, pp 60–66
Morales P, Sykes M, Prentice IC, Smith P, Smith B, Bugmann
H, Zierl B, Friedlingstein P, Viovy N, Sabaté S, Sánchez A,
Pla E, Gracia C, Sitch S, Arneth A, Ogee J (2005) Comparing and evaluating process-based ecosystem model
predictions of carbon and water fluxes in major European
forest biomes. Glob Change Biol 11:2211–2233
Muñoz Álvarez JM (coord) (2010) Vegetación de la Reserva de
la Biosfera y de los Espacios Naturales de Sierra Morena,
Consejerı́a de Medio Ambiente, Junta de Andalucı́a, Córdoba, Spain
Nagelkerke NJD (1991) A note on a general definition of the
coefficient of determination. Biometrika 78:691–692
Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ,
Mathesius U, Poot P, Purugganan MD, Richards CL,
Valladares F, van Kleunen M (2010) Plant phenotypic
plasticity in a changing climate. Trends Plant Sci
15:684–692
Nixon KC (2006) Global and neotropical distribution and
diversity of oak (genus Quercus) and oak forests. In:
Kappelle M (ed) Ecology and conservation of neotropical
Montane oak forests. Ecological studies series, vol 185.
Springer-Verlag, Berlin, pp 3–13
Olden JD, Jackson DA, Peres-Neto PP (2002) Predictive models
of fish species distributions: a note on proper validation and
chance predictions. Trans Am Fish Soc 131:329–336
Parmesan C, Yohen G (2003) A globally coherent fingerprint of
climate change impacts across natural systems. Nature
421:37–42
Rabasa SG, Granda E, Benavides R, Kunstler G, Espelta JM,
Ogaya R, Peñuelas J, Scherer-Lorenzen M, Gil W, Grodzki
W, Ambrozy S, Bergh J, Hódar JA, Zamora R, Valladares F
(2013) Disparity in elevational shifts of European trees in
response to recent climate warming. Glob Change Biol
19:2490–2499
Ruiz de la Torre J (2006) Flora mayor, Organismo autónomo
Parques Nacionales. Dirección General de Conservación
de la Naturaleza, Madrid
Ruiz-Labourdette D, Schmitz MF, Pineda FD (2013) Changes in
tree species composition in Mediterranean mountains
under climate change: indicators for conservation planning. Ecol Ind 24:310–323
Schröter D, Cramer W, Leemans R, Prentice IC, Araújo MB,
Arnell NW, Bondeau A, Bugmann H, Carter TR, Gracia
Carlos A, de la Vega-Leinert AC, Erhard M, Ewert F,
Glendining M, House J, Kankaanpää S, Klein RJT, Lavorel
S, Lindner M, Metzger MJ, Meyer J, Mitchell TD,
Reginster I, Rounsevell M, Sabaté S, Sitch S, Smith B,
Smith J, Smith P, Sykes MT, Thonicke K, Thuiller W, Tuck
G, Zaehle S, Zierl B (2005) Ecosystem service supply and
human vulnerability to global change in Europe. Science
310:1333–1337
Schwarz G (1978) Estimating the Dimension of a Model. Ann
Stat 6:461–464
Simeone MC, Piredda R, Papini A, Vessella F, Schirone B
(2013) Application of plastid and nuclear markers to DNA
barcoding of Euro-Mediterranean oaks (Quercus, Fagaceae): problems, prospects and phylogenetic implications.
Bot J Linn Soc 172:478–499
Sumarga E (2011) A comparison of logistic regression, geostatistics and Maxent for distribution modeling of a forest
endemic; a pilot study on lobel’s maple at Mt. Pizzalto,
Italy. Dissertation, Enschede, The Netherlands
Swets JA (1988) Measuring the accuracy of diagnostic systems.
Science 240:1285–1293
Thuiller W, Lavergne S, Roquet C, Boulangeat I, Lafourcade B,
Araujo MB (2011) Consequences of climate change on the
tree of life in Europe. Nature 470:531–534
Valle F (coord) (2004) Modelos de restauración forestal. Consejerı́a de Medio Ambiente, Junta de Andalucı́a, Sevilla,
Spain
Vericat P, Piqué M, Serrada R (2012) Gestión adaptativa al
cambio global en masas de Quercus mediterráneos. Centre
Tecnològic Forestal de Catalunya, Lleida
Vessella F, Schirone B (2013) Predicting potential distribution
of Quercus suber in Italy based on ecological niche models:
conservation insights and reforestation involvements. For
Ecol Manag 304:150–161
Vessella F, Simeone MC, Schirone B (2015) Quercus suber range
dynamics by ecological niche modelling: from the Last
Interglacial to present time. Quatern Sci Rev 119:85–93
Vuk M, Curk T (2006) ROC curve, lift chart and calibration plot.
Metodološkizvezki 3(1):89–108
123