Supporting Information
Uncertainties in the projection of species distributions related to
general circulation models
Eric Goberville1,2,3*, Grégory Beaugrand2,3,4, Nina-Coralie Hautekèete1, Yves Piquot1 and
Christophe Luczak3,5,6
1
Université Lille 1 - Sciences et Technologies (USTL), UMR 8198 GEPV, Laboratoire de
Génétique et Evolution des Populations Végétales, F-59655 Villeneuve d'Ascq, FR
2
Université Lille 1 - Sciences et Technologies (USTL), UMR 8187 LOG, Laboratoire
d’Océanologie et de Géosciences, 28 Avenue Foch, F-62930 Wimereux, FR
3
CNRS, UMR 8187 LOG, Laboratoire d’Océanologie et de Géosciences, 28 Avenue Foch, F-
62930 Wimereux, FR
4
SAHFOS (Sir Alister Hardy Foundation for Ocean Science), The Laboratory, Citadel Hill,
Plymouth PL1 2PB, UK
5
Université d’Artois, ESPE, Centre de Gravelines, 40 rue Victor Hugo - BP 129, 59820
Gravelines, FR
6
Université Lille Nord de France, FR
* To whom correspondence should be addressed:
E-mail: eric.goberville@univ-lille1.fr
Submitted to
Ecology and Evolution
1
Supplementary Figures and Tables
Table S1. Environmental data retrieved from the WorldClim dataset. Variables are derived
from the monthly temperature and rainfall values in order to generate more biologically
meaningful variables. The variables represent annual trends (e.g. mean annual temperature,
annual precipitation…), seasonality (as standard deviation for temperature and coefficient of
variation for precipitation) and extreme or limiting environmental factors. A quarter is a
period of three months (1/4 of the year). They are coded from 1 to 19 following the
indications on the WorldClim website (http://www.worldclim.org/).
2
Table S2. Environmental parameters retained from the WorldClim dataset (Table S1) after
application of the Escoufier procedure (See Materials and Methods).
3
Name
Radiative forcing
Pathway
SRES scenario with
similar median
temperature increase
by 2100
RCP2.6
Peak at ~ 3 W.m-2 before 2100
and then declines
Peak and decline
None
RCP4.5
~ 4.5 W.m-2 at stabilization after
2100
Stabilization without
overshoot
SRES B1
RCP6.0
~ 6 W.m-2 at stabilization after
2100
Stabilization without
overshoot
SRES B2
decades between 2060 and 2090, and slower during other periods of the 21
century
RCP8.5
> 8.5 W.m-2 in 2100
Rising
SRES A1FI
period between 2035 and 2080, and faster during other periods of the 21
century
Particular differences (RCPs vs SRES)
The ratio between temperature increase and net radiative forcing in 2100 is
0:88 C (W.m-2 ) for RCP2.6, whereas all other scenarios show a ratio of about
-2
0:62 C (W.m ). RCP2.6 is closer to equilibrium in 2100 than the other
scenarios
Median temperature in RCP4.5 rise faster than in SRES B1 until mid-century,
and slower afterwards.
Median temperature in RCP6.0 rise faster than in SRES B2 during the three
st
Median temperatures in RCP8.5 rise slower than in SRES A1FI during the
st
Table S3. The four RCPs and main similarities and differences between temperature
projections for SRES and RCPs. From Moss et al., (2010) and Rogelj et al., (2012).
4
Table S4. General Circulation Models (GCMs) used in this study. Detailed descriptions of the
different GCMs can be found in the references cited in the Table.
5
Betula nana
MDT
run 1
run 2
run 3
run 4
run 5
0.273
0.269
0.277
0.273
0.274
Castanea sativa
MDT
run 1
run 2
run 3
run 4
run 5
0.194
0.189
0.209
0.191
0.198
AUC
Mean
Min
Max
SD
0.804
0.826
0.813
0.809
0.810
0.771
0.803
0.789
0.777
0.792
0.832
0.850
0.845
0.842
0.825
0.010
0.009
0.010
0.010
0.008
AUC
Mean
Min
Max
SD
0.885
0.875
0.861
0.895
0.865
0.857
0.846
0.838
0.861
0.836
0.905
0.887
0.885
0.909
0.887
0.008
0.011
0.009
0.008
0.011
Table S5. Minimised Difference Threshold (‘MDT’) criterion and statistical summary of
AUC values from the ROC curve procedure; average (Mean), minimum (Min), maximum
(Max) and standard deviation (SD) of the AUC value for each species. Five model runs were
performed for each species where a different random partitioning of the occurrence data (70%
for calibration and 30% for validation) was used at each time.
6
Table S6. Table of the 25 simulations distributed within the three projected trends
(pessimistic, moderate and optimistic) for both species and the two periods 2010-2029 and
2080-2099. See the Methods section ‘Projections of the spatial changes in species
distribution’ for details on the categories. See Table S4 for the meaning of GCMs.
7
Betula nana
Castanea sativa
MPI-ESM-LR RCP2.6
CCSM4 RCP2.6
GISS-E2-R RCP4.5
MPI-ESM-LR RCP4.5
CCSM4 RCP4.5
GISS-E2-R RCP6.0
IPSL-CM5A-LR RCP2.6
MPI-ESM-LR RCP2.6
CNRM-CM5 RCP4.5
GISS-E2-R RCP4.5
IPSL-CM5A-LR RCP4.5
MPI-ESM-LR RCP4.5
IPSL-CM5A-LR RCP6.0
CNRM-CM5 RCP8.5
GISS-E2-R RCP8.5
CCSM4 RCP4.5
CSIRO-Mk3.6.0 RCP6.0
GISS-E2-R RCP6.0
CCSM4 RCP6.0
CNRM-CM5 RCP8.5
CCSM4 RCP8.5
GISS-E2-R RCP2.6
IPSL-CM5A-LR RCP2.6
CSIRO-Mk3.6.0 RCP4.5
IPSL-CM5A-LR RCP4.5
MPI-ESM-LR RCP4.5
IPSL-CM5A-LR RCP6.0
CNRM-CM5 RCP8.5
IPSL-CM5A-LR RCP8.5
MPI-ESM-LR RCP8.5
IPSL-CM5A-LR RCP4.5
HadGEM2-ES RCP4.5
CSIRO-Mk3.6.0 RCP6.0
CCSM4 RCP6.0
GISS-E2-R RCP8.5
-CSIRO-Mk3.6.0 RCP2.6
GISS-E2-R RCP2.6
HadGEM2-ES RCP2.6
CCSM4 RCP2.6
CSIRO-Mk3.6.0 RCP4.5
GISS-E2-R RCP6.0
CCSM4 RCP6.0
MPI-ESM-LR RCP8.5
--
CCSM4 RCP2.6
GISS-E2-R RCP4.5
IPSL-CM5A-LR RCP4.5
CSIRO-Mk3.6.0 RCP8.5
MPI-ESM-LR RCP8.5
-CSIRO-Mk3.6.0 RCP2.6
MPI-ESM-LR RCP2.6
GISS-E2-R RCP4.5
CSIRO-Mk3.6.0 RCP6.0
GISS-E2-R RCP6.0
CCSM4 RCP6.0
GISS-E2-R RCP8.5
HadGEM2-ES RCP8.5
--
CSIRO-Mk3.6.0 RCP8.5
HadGEM2-ES RCP8.5
MPI-ESM-LR RCP8.5
CCSM4 RCP8.5
-HadGEM2-ES RCP4.5
CCSM4 RCP4.5
CSIRO-Mk3.6.0 RCP6.0
HadGEM2-ES RCP6.0
CSIRO-Mk3.6.0 RCP8.5
IPSL-CM5A-LR RCP8.5
HadGEM2-ES RCP8.5
CCSM4 RCP8.5
--
GISS-E2-R RCP8.5
IPSL-CM5A-LR RCP8.5
HadGEM2-ES RCP8.5
-HadGEM2-ES RCP2.6
CCSM4 RCP2.6
CNRM-CM5 RCP4.5
HadGEM2-ES RCP4.5
CCSM4 RCP4.5
HadGEM2-ES RCP6.0
CSIRO-Mk3.6.0 RCP8.5
CCSM4 RCP8.5
--
CSIRO-Mk3.6.0 RCP4.5
MPI-ESM-LR RCP2.6
CNRM-CM5 RCP8.5
HadGEM2-ES RCP4.5
IPSL-CM5A-LR RCP8.5
GISS-E2-R RCP2.6
HadGEM2-ES RCP2.6
MPI-ESM-LR RCP4.5
CNRM-CM5 RCP4.5
GISS-E2-R RCP2.6
HadGEM2-ES RCP6.0
HadGEM2-ES RCP2.6
HadGEM2-ES RCP6.0
CSIRO-Mk3.6.0 RCP2.6
CNRM-CM5 RCP4.5
CSIRO-Mk3.6.0 RCP4.5
IPSL-CM5A-LR RCP2.6
CSIRO-Mk3.6.0 RCP2.6
IPSL-CM5A-LR RCP6.0
IPSL-CM5A-LR RCP2.6
IPSL-CM5A-LR RCP6.0
2090s
2020s
2090s
2020s
optimistic trends
2090s
moderate trends
2020s
pessimistic trends
Figure S1. Distribution map of the occurrence of Castanea sativa in Europe from the
European Forest Genetic Resources Programme (http://www.euforgen.org).
8
Figure S2. Results from the Escoufier procedure (See Materials and Methods).
Environmental factors are coded from 1 to 19 following the indications on the WorldClim
dataset (Table S1).
9
Figure S3. Modelled ecological niche of Betula nana assessed from NPPEN and based on
four environmental factors represented by pairs. a. Precipitation of the driest quarter and
Temperature annual range; b. Precipitation of the driest quarter and Annual mean
temperature; c. Precipitation of the coldest quarter and Temperature annual range. The
colorbar indicates the probability of occurrence of B. nana.
10
Figure S4. Modelled ecological niche of Castanea sativa assessed from NPPEN and based on
five environmental factors represented by pairs. a. Annual mean precipitation and Annual
mean temperature; b. Temperature seasonality and Annual mean temperature; c. Precipitation
seasonality and Annual mean temperature; d. Precipitation of the warmest quarter and Annual
mean temperature. The colorbar indicates the probability of occurrence of C. sativa.
11
Figure S5. Description of the ‘delta method’ procedure. GCM: General Circulation Model,
tmin: temperature minimum, tmax: temperature maximum. Adapted from Ramirez & Jarvis
(2010).
12
Figure S6. Variability in the probability of occurrence of a) Betula nana and b) Castanea
sativa for the periods 2010-2029 and 2080-2099 and three projected species trends. A
coefficient of variation was calculated for each geographical cell of our spatial domain, each
trend (pessimistic, moderate and optimistic) and each time period. Grey geographical cells
denote probabilities under the MDT criterion (see text).
13
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