Supporting Information
Improving niche projections of plant species under climate change: Silene acaulis on the British Isles as a case study
Alessandro Ferrarini, Mohammed H.S.A. Alsafran, Junhu Dai, Juha M. Alatalo* *E-mail:
[email protected]
Figure S1: Kernel density plots of the climate variables that proved most relevant for Silene acaulis presence (red density plots) and absence (blue density plots) on the British Isles.
Table S1: List of the candidate climate variables (20 annual, 16 seasonal, 48 monthly) available for modelling and projecting Silene acaulis presence/absence in the study area. Table S2: Results of the climate variable ranking. Climate variables are ranked from a high to a low relevance to Silene acaulis presence/absence in the study area. Table S3: Accuracy matrix of the naïve Bayes presence/absence model using 4 climate variables. Table S4: Model parameters that resulted in best accuracy for each niche model.
Table S5: Stepwise procedure employed to determine the best number of climate variables used as input for the naïve Bayes model of Silene acaulis presence/absence on the British Isles. The best classification accuracy (CA) was achieved using the first four climate variables determined through the variable filtering procedure. Adding further climate variables determined accuracy decreasing.
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Figure S1: Kernel density plots of the climate variables that proved most relevant for Silene acaulis presence (red density plots) and absence (blue density plots) on the British Isles.
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Table S1: List of the candidate climate variables (20 annual, 16 seasonal, 48 monthly) available for modelling and projecting Silene acaulis presence/absence in the study area. Annual variables
Description
MAT MWMT MCMT TD MAP MSP AH:M SH:M DD0 DD5 DD18 DD_18 NFFD FFP bFFP eFFP PAS
mean annual temperature (°C) mean warmest month temperature (°C) mean coldest month temperature (°C) temperature difference between MWMT and MCMT or continentality (°C) mean annual precipitation (mm) mean summer (May to Sept.) precipitation (mm) annual heat:moisture index (MAT+10)/(MAP/1000)) summer heat:moisture index ((MWMT)/(MSP/1000)) degree-days below 0°C, chilling degree-days degree-days above 5°C, growing degree-days degree-days below 18°C, cooling degree-days degree-days above 18°C, heating degree-days number of frost-free days frost-free period the Julian date on which FFP begins the Julian date on which FFP ends precipitation as snow (mm) between August in previous year and July in current year
EMT Eref CMD
extreme minimum temperature over 30 years Hargreaves reference evaporation Hargreaves climatic moisture deficit
Seasonal variables Tave_wt Tave_sp Tave_sm Tave_at Tmax_wt Tmax_sp Tmax_sm Tmax_at Tmin_wt Tmin_sp Tmin_sm Tmin_at PPT_wt PPT_sp PPT_sm PPT_at
Monthly variables Tave01 – Tave12 Tmax01 – Tmax12 Tmin01 – Tmin12 PPT01 – PPT12
Description winter (Dec.(prev. yr) - Feb.) mean temperature (°C) spring (Mar. - May) mean temperature (°C) summer (Jun. - Aug.) mean temperature (°C) autumn (Sep. - Nov.) mean temperature (°C) winter mean maximum temperature (°C) spring mean maximum temperature (°C) summer mean maximum temperature (°C) autumn mean maximum temperature (°C) winter mean minimum temperature (°C) spring mean minimum temperature (°C) summer mean minimum temperature (°C) autumn mean minimum temperature (°C) winter precipitation (mm) spring precipitation (mm) summer precipitation (mm) autumn precipitation (mm)
Description January - December mean temperatures (°C) January - December maximum mean temperatures (°C) January - December minimum mean temperatures (°C) January - December precipitation (mm)
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Table S2: Results of the climate variable ranking. Climate variables are ranked from a high to a low relevance to Silene acaulis presence/absence in the study area. Variable
Inf. Gain
r1
Gini
r2
Relief
r3
ANOVA
r4
Chi2
r5
Σ ri
Tmax_sp Eref Tmax_at DD5 Tmax04 Tmax05 Tmax09 Tmax10 Tave_sp Tmax03 Tave04 Tave05 Tmax_sm Tave09 Tmax06 DD_18 Tmax08 MAT Tave10 Tave_sm Tave03 Tave_at MWMT Tave07 Tave06 Tave08 Tmax07 Tmax11 Tmax02 Tmin04 Tmin05 DD18 PAS Tmin_sp Tmax_wt Tmin09 Tave11 Tmin07 Tmin_sm Tave02 Tmin10 Tmin06 Tmin08 Tmin03 EFFP
0.141 0.142 0.138 0.137 0.139 0.137 0.135 0.133 0.134 0.127 0.133 0.130 0.128 0.127 0.122 0.124 0.128 0.123 0.121 0.124 0.120 0.118 0.120 0.119 0.115 0.119 0.118 0.113 0.105 0.110 0.105 0.101 0.111 0.105 0.088 0.100 0.096 0.095 0.092 0.088 0.089 0.087 0.092 0.092 0.083
2 1 4 6 3 5 7 9 8 14 10 11 12 15 19 16 13 18 20 17 21 25 22 24 27 23 26 28 32 30 31 34 29 33 42 35 36 37 39 43 41 44 38 40 46
0.031 0.030 0.030 0.029 0.030 0.029 0.029 0.028 0.028 0.027 0.028 0.028 0.027 0.027 0.026 0.026 0.027 0.026 0.025 0.026 0.025 0.024 0.025 0.025 0.024 0.025 0.024 0.023 0.022 0.023 0.022 0.017 0.023 0.022 0.017 0.021 0.019 0.020 0.019 0.016 0.018 0.017 0.019 0.018 0.016
1 4 3 6 2 5 7 9 11 12 8 10 14 15 18 16 13 17 22 19 21 25 20 23 26 24 27 28 32 29 31 43 30 33 42 34 36 35 37 45 39 41 38 40 46
0.150 0.171 0.146 0.146 0.109 0.128 0.114 0.100 0.105 0.121 0.117 0.105 0.135 0.102 0.142 0.088 0.132 0.088 0.081 0.123 0.085 0.079 0.124 0.130 0.122 0.120 0.142 0.076 0.079 0.070 0.078 0.153 0.032 0.066 0.073 0.063 0.060 0.079 0.081 0.067 0.060 0.085 0.076 0.055 0.059
4 1 5 6 21 12 19 25 22 16 18 23 9 24 7 27 10 28 35 14 30 38 13 11 15 17 8 42 36 46 39 3 83 54 44 58 61 37 33 51 60 31 41 68 62
1054.6 962.8 989.9 1071.6 1003.6 893.0 920.8 964.0 986.3 924.7 773.0 815.2 655.2 886.6 732.1 896.8 618.8 891.3 905.2 623.8 865.1 864.4 585.9 593.6 663.3 586.3 597.0 790.3 816.7 761.9 655.0 160.6 1035.9 731.8 700.5 661.9 696.8 439.5 439.9 722.0 695.0 462.6 400.0 691.4 679.4
2 8 5 1 4 13 10 7 6 9 21 19 36 15 25 12 43 14 11 42 16 17 50 48 34 49 46 20 18 23 37 69 3 26 28 35 29 60 59 27 30 58 61 31 32
363.0 360.6 357.0 356.1 351.8 352.8 352.7 354.6 351.3 339.6 348.3 340.1 344.8 335.1 333.5 333.5 339.6 330.5 329.7 329.1 320.2 328.3 327.5 328.3 313.5 331.3 320.2 318.2 303.5 315.3 296.4 305.3 295.8 295.9 263.6 295.8 276.1 276.4 267.1 256.6 257.7 260.5 268.5 266.4 251.9
1 2 3 4 8 6 7 5 9 13 10 12 11 15 16 17 14 19 20 21 25 22 24 23 29 18 26 27 31 28 32 30 34 33 41 35 37 36 39 44 43 42 38 40 46
10 16 20 23 38 41 50 55 56 64 67 75 82 84 85 88 93 96 108 113 113 127 129 129 131 131 133 145 149 156 170 179 179 179 197 197 199 205 207 210 213 216 216 219 232
Overall ranking 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
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Tmax01 Tmax12 Tmin_at NFFD MCMT CMD FFP Tave_wt BFFP DD0 AHM Tave01 Tave12 SHM Tmin02 Tmin11 Tmin_wt EMNT Tmin01 Tmin12 PPT01 PPT03 PPT10 PPT02 PPT09 PPT_at PPT_wt PPT07 PPT11 PPT06 PPT_sm TD PPT08 MAP PPT05 PPT12 MSP PPT_sp PPT04
0.079 0.081 0.086 0.080 0.078 0.061 0.078 0.077 0.076 0.067 0.055 0.067 0.066 0.046 0.058 0.070 0.056 0.056 0.055 0.052 0.042 0.042 0.033 0.042 0.028 0.032 0.037 0.015 0.021 0.013 0.013 0.006 0.010 0.024 0.016 0.027 0.012 0.017 0.012
49 47 45 48 50 58 51 52 53 56 62 55 57 65 59 54 61 60 63 64 67 66 70 68 72 71 69 78 75 80 79 84 83 74 77 73 81 76 82
0.015 0.016 0.017 0.015 0.014 0.011 0.015 0.014 0.015 0.013 0.012 0.013 0.013 0.008 0.013 0.014 0.013 0.013 0.012 0.012 0.009 0.009 0.007 0.009 0.006 0.007 0.008 0.003 0.004 0.003 0.002 0.001 0.002 0.005 0.003 0.006 0.002 0.004 0.002
50 47 44 49 53 64 48 52 51 56 63 55 57 68 58 54 60 59 61 62 65 66 70 67 73 71 69 79 75 78 81 84 83 74 77 72 80 76 82
0.068 0.067 0.056 0.050 0.058 0.154 0.065 0.060 0.068 0.027 0.086 0.055 0.057 0.111 0.055 0.049 0.051 0.048 0.045 0.048 0.056 0.056 0.064 0.052 0.068 0.063 0.052 0.083 0.067 0.077 0.076 0.093 0.081 0.055 0.073 0.053 0.066 0.049 0.051
48 50 65 77 63 2 55 59 47 84 29 70 64 20 69 79 76 81 82 80 66 67 56 73 49 57 74 32 52 40 43 26 34 71 45 72 53 78 75
651.7 616.3 653.6 733.7 663.6 165.5 595.8 652.3 530.6 763.2 224.2 624.9 579.2 182.2 602.7 543.5 570.6 562.8 565.8 520.6 216.9 212.1 156.4 232.6 144.5 125.1 176.1 30.4 79.7 64.8 43.4 2.8 38.1 127.6 10.0 104.4 60.8 106.1 41.6
40 44 38 24 33 68 47 39 56 22 63 41 51 66 45 55 52 54 53 57 64 65 70 62 71 73 67 82 76 77 79 84 81 72 83 75 78 74 80
243.2 244.7 256.0 240.5 233.8 191.9 232.6 229.6 228.6 210.4 174.0 208.4 216.1 144.1 183.5 209.3 171.3 168.8 169.8 150.1 125.5 138.0 103.5 127.1 93.4 100.2 113.1 26.5 62.7 26.3 25.6 3.2 21.0 80.0 5.1 59.6 29.5 59.2 24.6
48 47 45 49 50 58 51 52 53 55 60 57 54 65 59 56 61 63 62 64 68 66 70 67 72 71 69 78 74 79 80 84 82 73 83 75 77 76 81
235 235 237 247 249 250 252 254 260 273 277 278 283 284 290 298 310 317 321 327 330 330 336 337 337 343 348 349 352 354 362 362 363 364 365 367 369 380 400
46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
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Table S3: Accuracy matrix of the naïve Bayes presence/absence model using 4 climate variables. Predicted Absence
Presence
∑
Absence
3341
59
3400
Presence
20
248
268
3361
307
3668
Actual ∑
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Table S4: Model parameters that resulted in best accuracy for each niche model.
Niche model
Parameters
adaboost
number of iterations=1000; classifier: MODLEM; classification strategy: strength and specificity with Laplace estimator
logistic regression
maximum iterations=1000; iteration termination=0.000001; maximum likelihood algorithm; up to 3-way terms
multiperceptron neural network
input nodes=4; hidden layers=1; hidden nodes=3; learning rate=0.3; momentum=0.8; neuron activation: hyperbolic tangent
naïve Bayes
type of prior: Laplace; size of LOESS window=100 observations
random forests
number of trees=1000; attributes considered at each split=10; limit depth of each tree=10; do not split subset smaller than 4
support vector machines
kernel: sigmoidal with parameters c=0 and g= (1 / n. of input variables); numerical tolerance=0.001; cost=1; iteration limit=1000
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Table S5: Stepwise procedure employed to determine the best number of climate variables used as input for the naïve Bayes model of Silene acaulis presence/absence on the British Isles. The best classification accuracy (CA) was achieved using the first four climate variables determined through the variable filtering procedure. Adding further climate variables determined accuracy decreasing.
N. variables
Input variables
CA (%)
1
Tmax_sp
88.36
2
Tmax_sp, Eref
91.59
3
Tmax_sp, Eref, Tmax_at
94.20
4
Tmax_sp, Eref, Tmax_at, DD5
97.84
5
Tmax_sp, Eref, Tmax_at, DD5, Tmax05
96.21
6
Tmax_sp, Eref, Tmax_at, DD5, Tmax05, Tmax04
95.08
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