1
2
3
4
5
6
7
Annex 1. Figure 1. Phylogenetic tree of the tree species included in the study. Tall trees: Fagus
sylvatica, Quercus petraea, Quercus humilis, Quercus faginea, Quercus suber, Quercus ilex,
Quercus pyrenaica, Castanea sativa, Betula pubescens, Betula pendula, Juniperus thurifera,
Pinus sylvestris, Pinus uncinata, Pinus nigra, Pinus pinea, Pinus pinaster, Pinus halepensis,
Abies alba. Short trees or shrubs: Ilex aquifolium, Sorbus aucuparia, Sorbus aria, Corylus
avellana, Acer opalus, Acer mospessulanum, Acer campestre, Juniperus phoenicea, Juniperus
communis.
8
1
9
Annex 2. Methods
10
11
12
Climatic suitability modelling. Climatic, topographic and radiation variables.
13
14
15
16
17
18
19
20
21
22
We undertook a climate regionalization through GIS modeling using ground data (1950–1998;
1068 thermometric and 1999 precipitation meteorological stations) from the Spanish weather
monitoring system (National Weather Institute; http://www.aemet.es) (see Ninyerola et al.,
2007a, b; Keenan et al., 2011 for details), resulting in 200m spatial resolution maps. Future
regionalized climate was obtained using an approximation based on differences between the
current climate (CRU; New et al., 1999) and the climate projection from the HadCM3 model
using the A1 storyline. Topographic variables (slope and aspect) were obtained through
standard GIS terrain modelling from a digital elevation model (DEM) at 200m spatial resolution.
Solar radiation was computed using astronomic equations, DEM and meteorological stations
(Pons and Ninyerola, 2008).
23
24
25
Climatic suitability modelling. Model calculations.
26
27
28
29
30
31
32
33
34
35
36
37
The climate niche model of each species was built after exploring a large number of variables.
Correlations between explanatory variables of species presence/absence above 0.70 led to the
elimination of one of the variables, selecting the most comprehensive and integrative one. For
each run, the GLM algorithm chose the best combination of variables according to the Akaike
Information Criterion (AIC). The most repeated set of variables in the 250 runs was chosen for
the final model, which consisted of a regression (suitability model) using the averaged
regression coefficients of the 250 runs. Model calibration was performed by using 80% of the
plots from each dataset. Accuracy was estimated with the remaining 20% of the plots, using the
area under the ROC curve parameter (AUC; Fielding and Bell, 1997). The final model accuracy
was computed using the average of the 250 runs, providing good results (AUC >0.85). Model
calculations were performed using R software (R Development Core Team 2010).
38
39
40
41
2
42
References
43
44
45
Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in
46
47
48
Kearney M, Phillips BL, Tracy CR, Christian KA, Betts G, Porter WP (2008) Modelling species
49
50
51
New M, Hulme M, Jones PD (1999) Representing twentieth century space-time climate
52
53
Ninyerola M, Pons X, Roure JM (2007a) Objective air temperature mapping for the Iberian
54
55
56
Ninyerola M, Pons X, Roure JM (2007b) Monthly precipitation mapping of the Iberian Peninsula
57
58
Pons X, Ninyerola M (2008) Mapping a topographic global solar radiation model implemented in
59
R Development Core Team (2010)
conservation presence/absence models. Env Conserv 24: 38-49
distributions without using species distributions: the cane toad in Australia under current and
future climates. Ecography 31: 423-434
variability. Part 1: development of a 1961–1990 mean monthly terrestrial climatology. J Clim 12:
829–856
Peninsula using spatial interpolation and GIS. Int J Clim 27: 1231–1242
using spatial interpolation tools implemented in a Geographic Information System. Theor Appl
Clim, 89: 195–209
a GIS and refined with ground data. Int J Clim 28: 1821–1834
3
Climatic Average
2
Cs
Bu
Ca
Qs
1
MAP
MAT
Pp
Ia
Axis 2
Ac
Pi
0
Qi
Ph
Jp
Pn
Jc
Qp
Sa
P/ETP
Ao
Qy Am
Qh
Qf
Fs
Su
Ps
Aa
Bp
-1
Jt
Pu
-2
-3
-2
-1
0
1
2
Axis 1
60
61
62
63
64
Annex 3. PCA ordination of average climatic characterization of species. The variance
accounted by the first and second components were 77.4% and 19.2%, respectively. MAT,
mean annual temperature; MAP, mean annual precipitation; P/ETP, ratio between precipitation
to potential evapotranspiration for June – August. Species abbreviations as in Figure 2.
65
4
Climatic Amplitude
2
Qi
Pi
Axis 2
1
Jc
BuQh
MAT SD
Qf
Ph
Jp
0
Jt
Pn
-1
Bp Su
Qp
Aa
Qy Ps
Qs
P/ETP SD
Ao
Pu
Ia
MAP SD Sa
Ca
Fs
Ac
Am
Pp
Cs
-2
-4
-3
-2
-1
0
1
2
Axis 1
66
67
68
69
70
71
Annex 4. PCA ordination of climatic amplitude characterization of species. The variance
accounted by the first and second components were 56.6% and 27.0%, respectively. MAT SD,
standard deviation of annual temperature; MAP SD, standard deviation of annual precipitation;
P/ETP SD, standard deviation of the ratio between precipitation to potential evapotranspiration
for June-August. Species abbreviations as in Figure 2.
72
73
5
Functional Traits
3
Qi
Jp
2
Qs
Axis 2
1
Jt
Ia
Jc
0
Ph
-1
Pu
Pp
-2
Pi
Pn
Qf Am
Sa WD
Qh
SM
SLA
LA Su
Nmass
Qy
Ca
Qp
Cs
Ao
Ac
Hmax
Bu
Fs
Ps
Bp
Aa
-3
-3
-2
-1
0
1
2
3
Axis 1
74
75
76
77
78
79
80
81
Annex 5. PCA ordination of functional traits of species. The variance accounted by the first and
second components were 45.8% and 23.7%, respectively. Hmax, maximum tree height; WD,
wood density; SLA, specific leaf area, Nmass, nitrogen content of leaves, LA, leaf area, SM,
seed mass. Species abbreviations as in Figure 2.
82
83
6