1
14. Supporting Information
2
3
S1. Climate and geology of the Maritime Alps
4
At 2000 m.a.s.l., from December to March, air temperature is just below 0 °C (ranging
5
from -1 to -3°C), with an annual mean temperature of 3.7°C (Walter & Ribolini, 2001).
6
Precipitation is more frequent above 1200 m and thunderstorms are common in summer (70
7
to110 days per year). Winter and autumn are the two seasons with the heaviest precipitation in
8
the Maritime Alps. The greatest 5-day accumulations of precipitation (up to 250 mm) occur
9
mostly in autumn. During the last decades, the Maritime Alps experienced long summer droughts
10
with frequent forest fires as well as repeated floods, especially during autumn (Boroneant et al.,
11
2006). Sunshine duration is particularly high and reaches approximately 2 700 h *y-1 (average
12
1971 - 2000). The dominant geological units are granite and gneiss but limestone can also be
13
found (e.g. NE part of Valle Stura).
14
15
S2. Details on the generation of the topo-climatic predictors
16
Digital elevation models
17
A 25-m digital elevation model (DEM) for France was obtained from IGN (Institut
18
Géographique National France, http.//www.ign.fr/) and for Italy, a 10-m DEM was provided by
19
SITAD (Sistema Informativo Territoriale Ambientale Diffuso, Italy, www.sistemapiemonte.it).
20
The Italian DEM was re-sampled using the Nearest Neighbour assignment algorithm (as in
21
Dullinger et al., 2012) to a 25m resolution in ArcMap (ESRI). Curvature and slope were directly
22
derived from the DEM (Table S2), whereas potential global solar radiation during the growing
23
season (June to September) was calculated using the ArcInfo custom codes as in Randin et al.
1
24
(2006; 2009).
25
Climatic variables
26
We calculated long-term monthly average temperature and the sum of precipitation for the
27
standard period 1971 - 2000. The selected climate stations provide at least ten years of records
28
within the standard period. For each 25 m cell of the landscape, we normalized the long-term
29
monthly temperature and precipitation values of the weather stations to 0 m a.s.l., using the lapse
30
rates of a linear model (monthly temperature or monthly sum of precipitation ~ elevation) for
31
each month and the digital elevation model (DEM). Residuals of the linear model were
32
interpolated on the wider region of the Maritime Alps (surface of 7720 km2) to avoid edge
33
effects, using inverse distance weighted interpolations (IDW; as in Randin et al., 2006) in
34
ArcGIS (ESRI) and then added to the normalized values (regression intercepts). Finally, the
35
spatially normalized and interpolated values of temperature and precipitation (representing
36
regression intercepts locally adjusted by the interpolated residuals) were re-projected to the
37
elevation using the 25 m DEM of the wider region of the Maritime Alps and the regression lapse
38
rates. The layers were then clipped to the size of the study area.
39
Custom ArcInfo codes (aml) are available for download at
40
http.//www.wsl.ch/staff/niklaus.zimmermann/programs/aml.html. (See also Table S2)
41
42
Monthly Potential Evapotranspiration (ETpTurc) was calculated using the formula of Turc
43
(1961).
44
ETpTurc (mm * day-1) = (0.4 * ((0.0239001* Rs ) + 50.) * (Ta / (Ta+15)) / 30)
45
Rs. daily global radiation (monthly avg) (kJ * mm-2 * day-1)
46
Ta. monthly average (daily) temperature (°C)
2
47
48
In ArcGIS raster calculator, ETpTurc was calculated as follows (e.g. for June).
49
ETpJune = Int((0.4 / 30) * ((0.0239001 * Float([RsJune])) + 50) * (Float([TaJune]) /
50
(Float([TaJune]) + 15)) * 30)
51
52
For the growing season (GS. June-September).
ETpGS = [ETpJune] + [ETpJuly] + [ETpAugust] + [ETpSeptember]
53
Moisture index during the growing season (MINDGS) was a measure of the water balance of an
54
area in terms of gains from precipitation (P) and losses from potential evapotranspiration (ETp).
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56
57
MINDGS = PGS - ETpGS (in mm per month)
In ArcGIS raster calculator for the growing season .
MINDGS = [MINDJune] + [MINDJuly] + [MINDAugust] + [MINDSeptember]
58
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Growing degree days with a 0°C threshold (GDD0) are defined as the days during which the
60
average daily temperature Ta is high enough allowing the plant to grow and are defined as:
61
(Monthly averaged temperature Ta > 0 °C) * (number of days for a given month).
62
The geographic layers of the monthly averaged temperature (Ta) were first re-classed in Raster
63
Calculator for a 0°C threshold, and then multiplied by the number of days of the month.
64
GDD0June = Int([TaJune > 0°C] * 30)
65
Then the sum of the growing degree days of the growing season was calculated as the sum of the
66
growing degree days of every month:
67
GDD0GS = [GDD0June] + [GDD0July] + [GDD0August] + [GDD0September]
68
69
Topographic variables
Custom ArcInfo codes (aml) are available for download at
70
http.//www.wsl.ch/staff/niklaus.zimmermann/programs/aml.html. (See also Table S2)
3
71
Solar radiation (kJ * m-2 * day-1; Srad) was first calculated in ArcInfo for each month of the
72
growing season (GS; i.e. June to September), and then multiplied by the number of days for each
73
month to obtain a proxy of the total amount of energy during the entire growing season.
74
SRadGS = [SRadJune] * 30 + [SRadJuly] * 31 + [SRadAugust] * 31 + [SRadSeptember] * 30
75
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Table S2.1 Summary of the geographic layers used as predictors in SDMs with units and
77
methods used to generate them.
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84
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Table S2.2 Pearson correlation coefficients between the modelling variables. Most variables'
86
pairs have low correlation coefficients (i.e. r < 0.381). The highest was 0.806 between moisture
87
index and growing degree days but less than 0.85 (critical threshold based on Elith et al., 2006).
88
We finally decided to keep them because they are the most physiologically meaningful variables.
89
In bold, the r values for the selected variables.
4
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93
94
95
96
97
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*Abbreviations: Grow= growing season (usually June-September or defined by a threshold of at least 20 days of temperature
above 0°C or 5°C depending on the species), ETP=potential evapotranspiration, DDEG=growing degree days, Solar_Rad=
solar radiation of the growing season, MIND= moisture index of the growing season
S3. Palaeoclimate reconstruction
Downscaling of the Palaeoclimate Dataset
The dataset was originally produced by Singarayer and Valdes (2010) from the HadCM3
100
atmosphere-ocean general circulation model (AOGCM) developed at the Hadley Centre. A
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temperature and precipitation dataset for the standard period 1971-2000 at a 10’ resolution was
102
obtained from the Climatic Research Unit (Mitchell et al. 2004) and used to derive past climate
103
anomalies from the palaeoclimate layers. These anomalies were then interpolated using a
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regularised spline to derive a smooth surface at a final resolution of 25 m by using Spatial
105
Analyst in ArcGIS (ESRI). The interpolated anomalies were finally added to the 25-m
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temperature and precipitation layers of current conditions. Growing degree days and moisture
107
index layers were then calculated for all time periods of the paleoclimate series.
108
109
S4. SDM calibration
110
Generealised Linear Models (GLM)
111
Up to second-order polynomials (linear and quadratic terms) were allowed for each
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predictor in GLMs, using the R library rms (http.//CRAN.R-project.org/package=rms), with the
5
113
linear term being forced in the model each time the quadratic term was retained. All models were
114
fit using GLMs in R (R 2.9.2, R Development Core Team 2008) and associated packages
115
available in CRAN (http.//cran.r-project.org).
116
Similar to Vicente et al., (2010), we calibrated a set of competing models and applied
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multi-model inference (MMI; Burnham & Anderson 2002). We used the corrected Akaike
118
information criterion of goodness to fit (AIC: Akaike 1973 and AICc: Shono 2000) to calculate
119
the Akaike weights. The ensemble of all model combinations including the five predictive
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variables was calculated as the sum of the individual models’ predictions weighted by Akaike
121
weights (model averaging), in order to account for the uncertainty within the modelling selection
122
process.
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124
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Table S4. Number of pseudoabsences, methods used for selecting pseudoabsences and number
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of models replication for each SDM technique. Choices of parameters followed tightly the
127
recommendation of Babet-Massin et al. (2012).
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129
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Table S5. Suitable land-cover types and their corresponding code in the CORINE Land Cover
133
database
134
http.//www.eea.europa.eu/data-and-maps/data#c12=corine+land+cover+version+13)
(shapefile
downloaded
on
16th
October
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141
142
143
144
145
146
147
148
149
150
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152
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156
7
2011,
available
at.
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Table S6. Number of suitable pixels and number of ka that have been predicted to be suitable by
158
the ensembles (consensus, majority and minority) of SDMs. (The number of ka does not
159
correspond to a chronological order and the pixels have not necessarily remained suitable for
160
consecutive ka, except for the ones suitable for all 22 ka).
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S7. Identification of potential core areas and identification of topo-climatic microrefugia
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The microrefugium identification criteria were based on the framework of Ashcroft et al
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(2012). In order to set a comparable scale between the three criteria, we used the standardised
166
residuals (z values), as in Ashcroft et al. (2012), for the average temperature of the growing
167
season.
168
(Value of a grid cell – Mean over the whole study area) / (Standard Deviation SD) (1)
169
170
(1) Isolation from the matrix (i.e. the surrounding area); calculation of the z values for the
171
averages of each climatic period. (Zmatrix 3pixels and Zmatrix 1km)
172
The z values of isolation from the matrix were calculated using a moving window
173
(hereafter MW) of 1 km (40-pixels radius) and at a finer scale with a moving window of 75 m
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(3-pixels radius Zmatrix
175
is the maximum dispersal distance a species such as S.florulenta can reach within a 1000 years'
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time frame, when considering a short-distance dispersal (SDD) kernel (Engler et al., 2009). The
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75-m MW was chosen in contrast to detect very local variations of the climatic parameters due to
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topographic complexity. Using different MW scales could also help in tracking microrefugia
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enclosed in larger isolated areas of the study domain. Computations of MW were done with
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Neighbour Focal statistics tool of Spatial Analyist in ArcGIS (ESRI).
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(2) Extreme temperature values (Ztemp)
182
We located areas that had shown the highest and lowest mean values for temperature within a
183
given climatic period (Table S4) (5 and 95 percentiles of z scores corresponding to the coldest
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and warmest areas respectively).
3pixels).
We opted for these two scales based on the assumption that 1 km
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(3) Stability of temperature conditions over time (Zvar interperiod and Zvar from current)
188
We
used
two
different
metrics.
189
a) The smallest changes compared to current conditions were calculated as the absolute values of
190
temperature change from the current conditions, for each of the time frames within a climatic
191
period (Zvar
192
whole, we summed the absolute values of the change of every time frame and then calculated Z-
193
scores based on this sum (1). The 5% percentiles of these Z-scores represent the locations with
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the most stable (lower 5%) and unstable (upper 5%) temperature conditions (R.basic package,
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http.//www.braju.com/R/).
196
b) The smallest changes between successive time frames (Zvar
197
similarly as above, but between one time frame and the next (e.g. 21 to 20 ka BP). We then
198
summed the absolute values of change of the corresponding time frames for a given period, and
199
calculated the corresponding z scores.
200
interperiod).
In order to capture the amount of change within a climatic period as a
from current)
were calculated
Finally, the Refugia Index (RI) was expressed as an average of the z values for each of
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the three criteria above and was calculated separately for the two isolation scales (75-m and
202
1km):
203
204
RI = (Ztemp + sign(Zmatrix 3cells).((Zvar interperiod+ Zvar from current ) / 2) + Zmatrix) / 3 (Eq. 2a)
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RI = (Ztemp + sign(Zmatrix 1km).((Zvar interperiod+ Zvar from current ) / 2) + Zmatrix) / 3 (Eq. 2b)
206
207
A negative RI represents a colder area than the surrounding landscape matrix, while a positive RI
208
a warmer one and hence potential cold and warm topo-climatic microrefugia. We then combined
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the lowest and highest RI at the two isolation scales to select areas with the highest and lowest RI
210
for each climatic period. For Late Pre-Glacial that was particularly cold, we considered both cold
211
and warm microrefugia: we looked for warm microrefugia because they would be needed by the
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species to survive the extremely cold temperatures of that cold period and the cold ones because
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populations managing to persist in those patches could still further survive during the warmer
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periods that followed. For the other climatic periods that were leading to a gradually warming
215
climate we considered only the cold ones before temperature became more stable (i.e. Atlantic
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and Subboreal & Mid-Subatlantic periods). Finally, all topo-climatic microrefugia selected fell in
217
areas predicted to have been suitable for the species for each of the climatic periods within the
218
21,000 years.
219
The importance of regional to local topography as mentioned by Dobrowski (2011) was
220
incorporated by adding the tendency of an area to experience cold air pooling (CAP) to the
221
criteria of Ashcroft et al. (2012) but was not incorporated in the RI. It was only used to filter the
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final selection based on the RI. Areas that favour the accumulation of cold air masses are
223
characterised by depressions and are relatively flat. We therefore considered as CAP prone areas
224
those with slope below 30° and curvature (i.e. slope of the slope) below 0 (i.e. concave), as
225
suggested by (Lundquist et al., 2008). We considered this as a more important variable for areas
226
isolated from the matrix for higher temperatures, especially during warmer periods (e.g. the
227
Atlantic).
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Figure S7: Microrefugia formed during each climatic period under the limited dispersal scenario
234
(LD) and their support in the species range shifts, persistence and re-colonization; (a)
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Microrefugia formed during Late Pre-Glacial, (b) Older Dryas, (d) Atlantic, (e) Subboreal &
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Mid-Subatlantic and their distribution within the potential suitable areas of each climatic period.
237
On panel (c), the established microrefugia in Late Pre-Glacial and Oldest Dryas that potentially
238
allowed the persistence of S. florulenta during the first identified predicted extinction period after
239
Late Pre-Glacial. (c) The expansion to the next time frame was possible within the 1km
240
expansion buffer around the microrefugia. (f) Current potential distribution and microrefugia of
241
past climatic periods that formed it after the last predicted extinction phase (re-colonization
242
microrefugia). (g) All the potential microrefugia from all climatic periods and known
243
occurrences and (h) (same as g) on all the predicted suitable areas across all ka. (i) microrefugia
244
that have been re-formed or persisted in the same location for at least two climatic periods,
245
species known occurrences, and current potential distribution.
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Figure S8. Frequency of patch sizes (in m2 ) of microrefugia formed in different climatic periods
252
that overlap (stable microrefugia) (a), (b) microrefugia assisting range shifts and expansion in
253
across the different climatic periods (stepping stones microrefugia), (c) microrefugia formed in
254
different climatic periods that shaped the current potential distribution (re-colonizing
255
microrefugia).
256
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Table S9. Surface colonized by the contribution of macrorefugia and microrefugia under the
258
limited (LD) and unlimited (UD) dispersal scenarios and expressed as a percentage of the
259
suitable surface for each 1-ka time frames (-20 to 0ka) predicted by the consensus of SDM
260
projections. In the microrefugia model, the presence of active microrefugia (indicated by *)
261
allowed the persistence of S. florulenta during absence phases predicted by SDMs and does not
262
correspond to the percentage of the suitable SDMs habitats since the latter predicts 0% suitable
263
pixels.
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S10. Model performance and predicted suitable pixels by ensemble of SDMs with two or one
274
climate variable.
275
Despite the fact that the Pearson correlation coefficient of the modelling variables was
276
lower than the standard set by Elith et al. (2006; Pearson correlation coefficient > 0.85 for
277
correlated variables) we wanted to exclude the effect potential collinearity of the climatic
278
variables (growing degree days and moisture index) in our models. The pattern of this version of
279
the projections during the extinction phases was similar to the previous version using both
280
climatic variables (see discussion). Regarding the models performance, GBM performed higher
281
(average TSS 0.934 with SD 0.009) compared to the previous version (average TSS = 0.832 with
282
SD = 0.010). MAXENT performed lower (average TSS 0.934 with SD 0.009) than the previous
283
version (TSS = 0.627 with SD = 0.019). The GLM projections in particular appeared more
284
restricted compared to the previous version and with slightly lower performance (average TSS =
285
0.53 with SD = 0.015 compared to TSS = 0.66 with SD = 0.011 of the previous version).
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Table S10. Comparison of predicted number of suitable pixels by the three models
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ensembles with both climate variables (i.e. growing degree days and moisture
298
index) and with growing degree days only.
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References
312
313
314
Akaike H (1973). Information theory and an extension of the maximum likelihood principle. In
Petrov BN and Csaki F (Eds.). Second international symposium on information theory (pp.
267-281). Budapest: Academiai Kiado
315
316
Anon (2004) ARCInfo version 9.0. Environmental Systems Research Institute, Redlands, CA,
USA
317
318
319
Ashcroft MB, Gollan JR, Warton DI, Ramp D (2012) A novel approach to quantify and locate
potential microrefugia using topoclimate, climate stability, and isolation from the matrix.
Global Change Biology, 18, 1866–1879.
320
321
322
Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species
distribution models: how, where and how many? Methods in Ecology and Evolution, 3, 1–
12.
323
324
325
Boroneant C, Plaut G, Giorgi F, Bi X (2006) Extreme precipitation over the Maritime Alps and
associated weather regimes simulated by a regional climate model . Present-day and future
climate scenarios. Theoretical and Applied Climatology, 99, 81–99.
326
327
Burnham, KP and Anderson, DR (2002) Model selection and multi model inference: a
practical information-theoretic approach, 2nd ed. Springer.
328
329
Dobrowski SZ (2011) A climatic basis for microefugia: the influence of terrain on climate.
Global Change Biology, 17, 1022–1035.
330
331
332
Darnault R, Rolland Y, Braucher R, Bourlès D, Revel M, Sanchez G, Bouissou S (2011) Timing
of the last deglaciation revealed by receding glaciers at the Alpine-scale: impact on
mountain geomorphology. Quaternary Science Reviews, 31, 127–142.
333
334
Dullinger S, Gattringer A, Thuiller W, et al. (2012) Extinction debt of high-mountain plants
under twenty-first-century climate change. Nature Climate Change, 2, 619–622.
335
336
Elith J, H. Graham C, P. Anderson R, et al. (2006) Novel methods improve prediction of species
’ distributions from occurrence data. Ecography, 2, 129–151.
337
338
339
340
341
342
343
344
345
Engler R, Randin CF, Vittoz P, et al. (2009) Predicting future distributions of mountain plants
under climate change: does dispersal capacity matter? Ecography, 32, 34–45.
Kumar, L, Skidmore, AK, Knowles E, (1997) Modelling topographic variation in solar radiation
in a GIS environment. International Journal of Geographical Information Science 11, 475–
497
Lundquist JD, Pepin N, Rochford C (2008) Automated algorithm for mapping regions of cold-air
pooling in complex terrain. Journal of Geophysical Research, 113, D22,27.
18
346
347
348
349
350
Mitchell TD, and Jones PD, (2005) An improved method of constructing a database of monthly
climate observations and associated high-resolution grids. International Journal of
Climatology, 25, 693-712
Randin, CF, Dirnböck T, Düllinger S, Zimmermann NE, Zappa M and Guisan A (2006) Are
species distribution models transferable in space? Journal of Biogeography, 33,1689-1703
Randin CF, Jaccard H, Vittoz P, Yoccoz NG, Guisan A (2009) Land use improves spatial
predictions of mountain plant abundance but not presence-absence. Journal of Vegetation
Science 20(6):996-1008
Singarayer, J S, Valdes P J, Friedlingstein P, Nelson S, and Beerling DJ (2011) Late Holocene
methane rise caused by orbitally controlled increase in tropical sources. Nature, 470
(7332). pp. 82-85. ISSN 1476-4687
Shono H (2000). Short Paper Efficiency of the finite correction of Akaike’ s Information
Criteria. Fisheries Science, 66, 608–610.
351
352
353
354
355
356
Turc L, 1961. Evaluation de besoins en eau d’ irrigation, ET potentielle, Annales Agronomiques,
12,13-49
357
358
359
Walter F, Ribolini A (2001) late glacial to holocene deglaciation of the colle del vei del bouccolle del sabbione area (Argentera Massif, Maritime Alps, Italy-France). Geografia Fisica e
Dinamica Quatenaria, 24, 141–156.
360
361
Zeverbergen, LW, and Thorne CR (1987). Quantitative Analysis of Land Surface Topography.
Earth Surface Processes and Landforms, 12, 47–56.
362
363
364
Zimmermann NE, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland:
species versus community approach. Journal of Vegetation Science, 10, 469–482.
365
Digital Elevation Models
366
367
368
369
France: IGN (Institute National de l’Information Géographique et Forestière; http://www.ign.fr/ )
Italy:
Area
SIT
(Sistemi
Informativi
Territoriali
Negozio
cartografico;
http://www.csipiemonte.it )
CORINE Land Cover, v. 13 (2006), http://www.eea.europa.eu/
Vicente J, Alves P, Randin C, Guisan A, Honrado J (2010) What drives invasibility? A multimodel inference test and spatial modelling of alien plant species richness patterns in
northern Portugal, Ecography, 33, 1081–1092.
370
19
371
Software
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
ESRI 2009. ArcGIS Desktop: Release 9.3.1. Redlands, CA: Environmental Systems Research
Institute.
R Development Core Team (2012). R: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
http://www.R-project.org/.
R packages used
Harrell FE Jr (2012). rms Regression Modelling Strategies. R package version 3.5-0.
http://CRAN.R-project.org/package=rms
Ridgeway G (2012). gbm Generalized Boosted Regression Models. R package version 1.6-3.2.
http://CRAN.R-project.org/package=gbm
Hijmans RJ & van Etten J (2012) raster Geographic analysis and modelling with raster data. R
package version 1.9-92. http://CRAN.R-project.org/package=raster
Keitt TH, Bivand R, Pebesma E, and Rowlingson B (2012). rgdal Bindings for the Geospatial
Data Abstraction Library. R package version 0.7-11. http://CRAN.R-project.org/package=rgdal
Lewin-Koh NJ, Bivand R, contributions by Pebesma EJ, Archer E, Baddeley A, et al., (2012).
maptools Tools for reading and handling spatial objects. R package version 0.8-14.
http://CRAN.R-project.org/package=maptools
Urbanek S (2011). rJava Low-level R to Java interface. R package version 0.9-3.
http://CRAN.R-project.org/package=rJava
Hijmans RJ, Phillips S, Leathwick J and Elith J (2012). dismo Species distribution modelling. R
package version 0.7-17. http://CRAN.R-project.org/package=dismo
Bengtsson H (2012). R.basic [R] Class Library - Stand-alone basic functions [DEPRECATED].
R package version 0.51.0. http://www.braju.com/R/
Engler R, Hordijk W and Pellissier L (2012). MigClim Implementing dispersal into species
distribution models. R package version 1.2. http://CRAN.R-project.org/package=MigClim
VanDerWal J, Falconi L, Januchowski S, Shoo L and Storlie C (2012). ENMTools Species
Distribution Modelling Tools: Tools for processing data associated with species distribution
modelling exercises. R package version 1.1-12. http://CRAN.R-project.org/package=ENMTools
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