Supplementary information File S1 Supplementary table S1. The source of the distribution records used in the present study Asociación Herpetológica Española. Base de datos de Anfibios y Reptiles de España. http://www.herpetologica.es/programas/base-de-datos-herpetologica  BazNat - base de données naturalistes partagée en Midi-Pyrénées. http://www.baznat.net Geniez. P.. Cheylan. M.. 2012. Les Amphibiens et les Reptiles du Languedoc-Roussillon et régions limitrophes. Atlas biogéographique. Biotope and Muséum national d’Histoire naturelle de Paris. Mèze and Paris. Cistude Nature. 2010. Guide des amphibiens et reptiles d'Aquitaine. Association Cistude Nature. Le Haillan. Bergerandi, A., Arzoz, M. J. 1991. Euproctus asper en Guipúzcoa. Munibe (Ciencias Naturales-Natur Zientziak), 43, 123. Clergue-Gazeau, M., Martínez-Rica, J. P. 1978. Les différents biotopes de l'urodèle pyrénéen, Euproctus asper. Bulletin de la Société d’Histoire Naturelle de Toulouse , 114 (3-4), 461471. Gosá, A., Bergerandi, A. 1994. Atlas de distribución de los Anfibios y Reptiles de Navarra. Munibe (Ciencias Naturales-Natur Zientziak) 46, 109-189. Maluquer-Margalef, J. 1983. Fauna herpetològica de les serralades exteriors del prepirineu occidental de Catalunya. Miscel·lània Zoologica 7, 117-129. Maluquer-Margalef, J. 1984. Nouvelles données sur la répartition sous-pyrénéen d'Euproctus asper (Dugès 1852). Bulletin de la Société Herpetologique de France 29, 38-43

Supplementary table S2. Explanation of MIGCLIM settings as provided by Engler et al. (2012) Parameter Species initial distribution [iniDist] Habitat suitability map(s) [hsMap] Reclassification threshold [rcThreshold]

Environmental change step number [envChgSteps]

Dispersal step number [dispSteps]

Dispersal kernel [dispKernel]

Propagule production potential [propaguleProd];

Description A layer of integer, binary, values indicating whether a given cell is initially hosting the species (1) or not (0). One or more layers indicating the habitat suitability of a given cell. unsuitable' habitats. Cells with values ≥ threshold are reclassified as 100% suitable while cells with values < threshold are reclassified as 0% suitable. [rcThreshold] must be an integer number in the range [0:1000]. If rcThreshold = 0, then habitat suitability values are not reclassified but are instead considered as habitat invasibility values (probability of a cell to become colonized given its habitat = habitat suitability/1000). Habitat with higher suitability have more likelihood to become colonized), the invasibility of a cell (e.g. the presence of another species can act as a competitor or facilitator), or both. Note that the invasibility values are interpreted by the model as an absolute probability of presence conditional on the species dispersing to the cell (e.g. all other things being equal, a cell with habitat suitability of 600 is twice as likely to be colonized as a cell with habitat suitability of 300). Number of times the habitat suitability layer should be updated within a simulation. This value must be equal to the number of habitat suitability layers available. Simulations without environmental change can be carried out by setting envChgSteps = 1. Number of times dispersal should be simulated within each environmental change step. The total number of dispersal steps in a simulation is thus equal to [dispSteps] X [envChgSteps]. Vector of values indicating the probability of a source cell to disperse values that are non-integer numbers (e.g. diagonals) are rounded to their closest integer number and attributed to that distance class. The probability of a source cell to produce propagules as a function of time

[iniMatAge]

Barriers to dispersal [barrier]; [barrierType]

Long distance dispersal [lddFreq]; [lddMinDist]; [lddMaxDist]

[replicateNb]

since the cell became colonized. This is specified via 2 parameters: initial production for each age between initial and full maturity [propaguleProd]. This parameter can be used as a proxy for population growth in the cell, or for instance to refl ect that a species might need several years before starting to produce propagules, and even more time to reach its full reproductive potential. The time unit is a dispersal step, which will usually represent one year. Layer of integer, binary, values indicating whether a given cell is a barrier to dispersal (1) or not (0). 'Barrier' cells are considered as permanently unsuitable for the species, but unlike regular unsuitable cells, they also impede dispersal across them (see Engler et al. 2012) Long distance dispersal events are randomly generated with a user-defined frequency [lddFreq] within a used-defined distance range [lddMinDist, lddMaxDist]. The frequency of long distance dispersal events is also Long distance dispersal events aim at representing non-standard ways of propagule dispersal. Number of times the dispersal simulation is to be replicated.

Supplementary table S3. CORINE landcover classes used in the present study. Dispersal barriers indicate if the class has been used as a barrier in the present study. Class ID

Class

Description

Dispersal barrier

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Artificial surfaces Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Agricultural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas

Continuous urban fabric Discontinuous urban fabric Industrial or commercial units Road and rail networks and associated land Port areas Airports Mineral extraction sites Dump sites Construction sites Green urban areas Sport and leisure facilities Non-irrigated arable land Permanently irrigated land Rice fields Vineyards Fruit trees and berry plantations Olive groves Pastures Annual crops associated with permanent crops Complex cultivation patterns Land principally occupied by agriculture, with significant areas of natural vegetation Agro-forestry areas Broad-leaved forest Coniferous forest Mixed forest Natural grasslands

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No No No No No No No No No

27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Forest and semi natural areas Wetlands Wetlands Wetlands Wetlands Wetlands Water bodies Water bodies

Moors and heathland Sclerophyllous vegetation Transitional woodland-shrub Beaches, dunes, sands Bare rocks Sparsely vegetated areas Burnt areas Glaciers and perpetual snow Inland marshes Peat bogs Salt marshes Salines Intertidal flats Water courses Water bodies

No No No Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes No

Supplementary table S4. The performance for the present and future SDMs according to the AUC and True Skill Statistic (TSS). Model Present 2020 2030 2040 2050 2060 2070 2080 Average        

 

Training AUC 0.942 0.940 0.944 0.941 0.940 0.942 0.943 0.942 0.942

Test AUC 0.934 0.932 0.938 0.934 0.931 0.934 0.935 0.934 0.934

TSS 0.810 0.863 0.788 0.891 0.810 0.812 0.836 0.782 0.824

 

Supplementary table S5*. Percent contribution and permutation importance (%) of the climatic predictor variables for the species distribution models of Calotriton asper in the Pyrenees. (please refer to the main text for the description of variables).

Variable BIO5 BIO9 BIO2 BIO13 BIO15 BIO3** BIO8 BIO6

Percent contribution 56.7 14.6 9.3 9.1 7.7 1.0 0.9 0.7

Permutation importance 2.5 11.6 26.7 16.8 29.1 3.8 2.6 6.9

*The following table gives estimates of relative contributions of the environmental variables to the Maxent model. To determine the first estimate, in each iteration of the training algorithm, the increase in regularized gain is added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of lambda is negative. For the second estimate, for each environmental variable in turn, the values of that variable on training presence and background data are randomly permuted. The model is reevaluated on the permuted data, and the resulting drop in training AUC is shown in the table, normalized to percentages. As with the variable jackknife, variable contributions should be interpreted with caution when the predictor variables are correlated. Values shown are averages over replicate runs. ** The most dissimilar variable in the study region (see Supplementary information Figure S3)

Supplementary table S6. Sample localities of Calotriton asper in the Pyrenees used for assessing loss of genetic diversity at the mtDNA level. Sequences of a single mitochondrial gene (cytochrome b) were obtained from GenBank. Locality

Country

Code

Nucleotide diversity Haplotype

Number of samples

Auriac Bernard Berga Betharram Bujaruelo Cailla Genie Longue Irati Isaba Pto. Montrepos Ordino Pas du Loup Portalet Port du Rat San Juan de la Peña La Cerdanya Vall Fosca Valle de Pineta Vidrà Vilanova de Meià Zuriza

France France Spain France Spain France France France France Spain Andorra France France France Spain Spain Spain Spain Spain Spain Spain

AU BD Ber BM Buj CA GL Ira Isf Mon Ord PL Pof Por Saj Top Vaf Vap Vid Vim Zur

0 0 0 0 0.00084 0 0 0.00305 0.0003 0.00045 0.00115 0 0 0 0.00084 0 0 0.00126 0 0 0.00128

7 10 21 10 36 13 10 24 18 24 34 10 13 12 22 19 21 26 15 18 21

H[1] H[1] H[2] H[1] H[1]H[3] H[1] H[1]

H[1]H[2]H[5] H[2]H[4] H[1]H[2] H[1]H[2] H[1] H[1] H[1] H[1]H[6] H[2] H[7] H[3]H[8] H[2] H[2] H[2]H[8]

Supplementary table S7. Sample localities of Calotriton asper in the Pyrenees used for assessing loss of genetic diversity (mean expected heterozygosity, He) in populations sampled using AFLP loci. Sequences were obtained from Milá et al. (2010)

Locality

Country

Code

He

Number of samples

Arcouzan Auriac Bernard Betharram Cailla Courbiere Cass-Rats Font de Dotz Genie Longue Irati Labouiche Olhadoko Pas du Loup Ribaui Siech Valmanya Vicdessos

France France France France France France France France France France France France France France France France France

AR AU BD BM CA CO CR FD GL IR LA OL PL RI SI VA VI

0.104 0.006 0.053 0.026 0.032 0.057 0.039 0.040 0.056 0.053 0.038 0.051 0.019 0.105 0.051 0.019 0.048

9 7 26 30 23 6 19 4 16 15 17 13 18 14 7 10 7

Supplementary table S8. Extractions of MIGCLIM model output (% of total cells) of each dispersal scenario in a buffer of seven kilometres (70 years times 100 m dispersal) surrounding populations with nucleotide diversity (>0) or unique haplotypes. No dispersal barrier Population Buj2 Ira1 Isf1 Mon Ord Saj1 Vaf1 Vap2 Zur2

Nucleotide diversity 0.00084 0.00305 0.00030 0.00045 0.00115 0.00084 0 0.00126 0.00128

Stable 17.7 0.0 23.0 0.0 12.3 0.0 11.9 19.7 39.4

Potential suitable 3.0 0.0 0.2 0.6 0.0 0.0 0.0 3.5 0.0

2020 0 0 0 0 0 0 0 0 0

2030 4.4 0 0 0 2.5 0 1.6 3.7 0.2

2040 4.5 0 0 0 1.9 0 1.1 2.6 0

2050 2.9 0 0.6 0.0 1.2 0 0.5 0.9 1.2

2060 1.8 0 0.4 0 0.4 0 0.2 0.6 0

2070 1.6 0 0.1 0 0.5 0 0.3 0.4 0

2080 4.4 0 1.0 0 0.3 0 0.3 4.0 0.4

Dispersal 19.5 0 2.2 0 6.8 0 3.9 12.2 1.8

Lost 52.5 80.4 62.3 55.9 73.5 21.3 75.5 52.1 58.8

Unsuitable 7.3 19.6 12.3 43.4 7.4 78.7 8.7 12.5 0.0

Extinct *** * ** * ** * *** *** **

Strong dispersal barrier Buj2 Ira1 Isf1 Mon Ord Saj1 Vaf1 Vap2

0.00084 0.00305 0.00030 0.00045 0.00115 0.00084 01 0.00126

17.7 0.0 23.0 0.0 12.3 0.0 11.9 19.7

3.0 0.0 0.1 0.6 0.0 0.0 0.0 3.5

0 0 0 0 0 0 0 0

4.4 0 0 0 2.5 0 1.6 4.8

4.5 0 0 0 1.9 0 1.1 2.5

3.0 0 0.6 0 1.2 0 0.5 1.0

1.8 0 0.4 0 0.4 0 0.1 0.5

1.6 0 0.1 0 0.5 0 0.3 0.4

4.3 0 1.0 0 0.2 0 0.3 4.0

19.6 0 2.2 0 6.8 0 3.9 13.2

52.5 80.4 62.3 55.9 73.5 21.3 75.5 52.4

7.3 19.6 12.3 43.5 7.4 78.7 8.7 11.2

*** * ** * ** * *** ***

Zur2

0.00128

39.4

0.0

0

0.2

0

1.2

0

0

0.4

1.8

58.8

0.0

**

1

Unique haplotype Haplotypes are only found in two populations *Extinct under all scenarios **No direct dispersal opportunities. Extinct under no or limited dispersal opportunities ***Stable 2

Supplementary table S8. Extractions of MIGCLIM model output (% of total cells) of each dispersal scenario in a buffer of seven kilometres (70 years times 100 m dispersal) surrounding populations with expected heterozygosity (He) of sampled populations. No dispersal barrier Population AR K = 4 AU K = 4 BD K = 4 BM K = 2 CA K = 4 CO K = 4 CR K = 4 FD K = 4 GL K = 2 IR K = 1 LA K = 4 OL K = 1 PL K = 3 RI K = 4 SI K = 4 VA K = 4 VI K = 5

He 0.104 0.006 0.053 0.026 0.032 0.057 0.039 0.040 0.056 0.053 0.038 0.051 0.019 0.105 0.051 0.019 0.048

Stable 47.5 0.0 0.0 4.8 8.0 69.0 0.0 0.0 13.4 0.0 0.0 7.0 0.0 57.9 34.1 9.4 59.3

Potential suitable 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

See next page for results Strong dispersal barrier

2020 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.5 0.0

2030 4.9 0.0 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.5 0.0 1.0 5.8

2040 2.9 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.9 0.0 0.4 0.9

2050 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.7 0.0 0.0 0.2

2060 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0

2070 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2080 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dispersal 8.5 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.6 0.0 3.5 5.9

Lost 41.1 11.6 70.5 26.1 71.2 29.0 48.0 51.4 57.2 80.5 60.3 76.8 52.9 33.6 0.0 0.0 0.0

Unsuitable 0.0 88.4 29.5 69.1 20.8 0.0 52.0 48.6 29.4 19.5 39.7 16.2 47.1 0.0 65.9 86.7 33.8

Extinct *** * * ** ** *** * * ** * * ** * ** ** ** **

 

Strong dispersal barrier Population He Stable Potential suitable 2020 2030 2040 2050 K=4 AR 0.104 47.5 0.0 0.0 4.9 2.7 2.9 AU K = 4 0.006 0.0 0.0 0.0 0.0 0.0 0.0 K=4 BD 0.053 0.0 0.0 0.0 0.0 0.0 0.0 K=2 BM 0.026 4.8 0.0 0.0 0.0 0.0 0.0 K=4 CA 0.032 8.0 0.0 0.0 0.0 0.0 0.0 CO K = 4 0.057 69.0 0.0 0.0 1.2 0.8 0.0 CR K = 4 0.039 0.0 0.0 0.0 0.0 0.0 0.0 K=4 FD 0.040 0.0 0.0 0.0 0.0 0.0 0.0 K=2 GL 0.056 13.4 0.0 0.0 0.0 0.0 0.0 IR K = 1 0.053 0.0 0.0 0.0 0.0 0.0 0.0 K=4 LA 0.038 0.0 0.0 0.0 0.0 0.0 0.0 K=1 OL 0.051 7.0 0.0 0.0 0.0 0.0 0.0 K=3 PL 0.019 0.0 0.0 0.0 0.0 0.0 0.0 RI K = 4 0.105 57.9 0.0 0.0 4.5 1.9 1.7 SI K = 4 0.051 34.1 0.0 0.0 0.0 0.0 0.0 K=4 VA 0.019 9.4 0.0 2.5 1.0 0.3 0.0 K=5 VI 0.048 59.3 0.0 0.0 5.7 1.0 0.2 *Extinct under all scenarios  **No direct dispersal opportunities. Extinct under no or limited dispersal opportunities  ***Stable   K  assignment to K = 5 cluster 

2060 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.0

2070 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

2080 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Dispersal 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.5 0.0 3.6 5.9

Lost 41.1 11.6 70.3 26.1 71.2 29.0 48.0 51.4 57.2 80.5 60.0 76.7 52.4 33.6 64.2 85.5 33.8

Unsuitable 0.0 88.4 29.7 69.1 20.8 0.0 52.0 48.6 29.4 19.5 40.0 16.4 47.6 0.0 1.6 1.2 0.0

Extinct *** * * ** ** *** * * ** * * * * ** ** ** **