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tidysdm overview
SDMs with tidymodels
Species Distribution Modelling relies on several algorithms, many of
which have a number of hyperparameters that require turning. The
tidymodels universe includes a number of packages
specifically design to fit, tune and validate models. The advantage of
tidymodels is that the models syntax and the results
returned to the users are standardised, thus providing a coherent
interface to modelling. Given the variety of models required for SDM,
tidymodels is an ideal framework. tidysdm
provides a number of wrappers and specialised functions to facilitate
the fitting of SDM with tidymodels.
This article provides an overview of the how tidysdm
facilitates fitting SDMs. Further articles, detailing how to use the
package for palaeodata, fitting more complex models and how to
troubleshoot models can be found on the tidisdm
website. As tidysdm relies on tidymodels,
users are advised to familiarise themselves with the introductory
tutorials on the tidymodels
website.
When we load tidysdm, it automatically loads
tidymodels and all associated packages necessary to fit
models:
library(tidysdm)
#> Loading required package: tidymodels
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom 1.0.6 ✔ recipes 1.0.10
#> ✔ dials 1.2.1 ✔ rsample 1.2.1
#> ✔ dplyr 1.1.4 ✔ tibble 3.2.1
#> ✔ ggplot2 3.5.1 ✔ tidyr 1.3.1
#> ✔ infer 1.0.7 ✔ tune 1.2.1
#> ✔ modeldata 1.3.0 ✔ workflows 1.1.4
#> ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
#> ✔ purrr 1.0.2 ✔ yardstick 1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/
#> Loading required package: spatialsample
Accessing the data for this vignette: how to use
rgbif
We start by reading in a set of presences for a species of lizard
that inhabits the Iberian peninsula, Lacerta schreiberi. This
data is taken from GBIF Occurrence Download (6 July 2023) https://doi.org/10.15468/dl.srq3b3. The dataset is
already included in the tidysdm package:
data(lacerta)
lacerta
#> ID latitude longitude
#> 1 858029749 42.57386 -7.093272
#> 2 858029738 42.57386 -7.093272
#> 3 614631090 41.36433 -7.901420
#> 4 614631085 41.33614 -7.806970
#> 5 614631083 41.33599 -7.808340
#> 6 614631080 41.38818 -7.830690
#> 7 614631072 41.37781 -7.813690
#> 8 614559731 40.34988 -7.702352
#> 9 614559728 40.38260 -7.701418
#> 10 614559657 40.35550 -7.558990
#> 11 614559646 40.29421 -7.650721
#> 12 614559638 40.31025 -7.750595
#> 13 614559626 40.30913 -7.754499
#> 14 614559614 40.30823 -7.755680
#> 15 614559580 40.36137 -7.652468
#> 16 614559536 40.32880 -7.675835
#> 17 614559494 40.32503 -7.683771
#> 18 614559485 40.32780 -7.677855
#> 19 4138510168 42.02203 -8.128677
#> 20 4138300594 41.99143 -8.268817
#> 21 4137774808 41.59728 -8.736125
#> 22 4137647526 41.28201 -8.731018
#> 23 4137647525 41.82842 -7.917928
#> 24 4134139940 40.69223 -8.162669
#> 25 4133852702 40.06937 -8.268567
#> 26 4121487317 41.68082 -7.713207
#> 27 4121307904 41.88402 -8.252778
#> 28 4121184631 40.84378 -7.726580
#> 29 4121124321 41.09547 -8.488889
#> 30 4116626006 42.04772 -8.503785
#> 31 4116285362 40.85782 -8.281194
#> 32 4116236838 40.66703 -7.900817
#> 33 4116092213 41.61971 -8.087063
#> 34 4112181181 41.70629 -8.096636
#> 35 4112023856 40.89950 -8.236026
#> 36 4111883952 41.63347 -7.574793
#> 37 4111614197 40.32288 -7.602690
#> 38 4103336935 40.08139 -8.204578
#> 39 4103238233 40.09843 -8.235022
#> 40 4102652095 41.87182 -8.208900
#> 41 4102603846 40.92872 -8.257428
#> 42 4102587117 41.86692 -8.216805
#> 43 4097012311 40.76552 -8.156668
#> 44 4096983593 40.92807 -8.258583
#> 45 4096753776 40.92810 -8.258452
#> 46 4080894369 40.37558 -8.368415
#> 47 4080783334 41.88167 -8.694533
#> 48 4076368267 40.63692 -8.439818
#> 49 4076093306 40.15494 -8.220965
#> 50 4076035760 40.76200 -8.553856
#> 51 4058226968 40.39995 -7.588924
#> 52 4058037946 40.40788 -7.562885
#> 53 4057914333 40.29825 -7.767162
#> 54 4056787598 43.40633 -5.339476
#> 55 4056745747 41.76657 -8.642622
#> 56 4056481420 40.40565 -7.891960
#> 57 4056306431 37.33848 -8.572752
#> 58 4046414634 40.84864 -8.382517
#> 59 4018170017 40.84750 -8.474317
#> 60 4015144485 41.16098 -8.482696
#> 61 4014974842 41.16116 -8.482167
#> 62 4006699733 41.80984 -8.131572
#> 63 3997288194 43.25489 -8.214883
#> 64 3997172501 43.45000 -4.980000
#> 65 3997131929 43.25482 -8.214870
#> 66 3997050991 43.45000 -4.980000
#> 68 3996318299 43.31143 -8.541861
#> 69 3996029768 43.25493 -8.215063
#> 70 3995883566 43.25478 -8.214628
#> 71 3995742860 40.24523 -5.604502
#> 72 3994052177 40.40062 -7.587015
#> 73 3966586163 37.32890 -8.583760
#> 74 3947369148 40.19553 -8.236308
#> 75 3912316870 41.09898 -8.560330
#> 76 3912179831 41.31493 -8.257731
#> 77 3907446980 42.83405 -7.066258
#> 78 3907284763 43.12507 -4.880099
#> 79 3907157374 43.22843 -6.046033
#> 80 3907117102 40.34128 -5.134518
#> 81 3906896706 43.60513 -5.888642
#> 82 3906336401 40.19971 -5.742140
#> 83 3906255544 40.33909 -5.156703
#> 84 3906081395 40.27000 -5.240000
#> 85 3906018124 41.77160 -8.188200
#> 86 3905579314 42.36183 -8.549850
#> 87 3905503521 43.31927 -8.521358
#> 88 3904992758 40.30552 -5.238461
#> 89 3904847047 42.82917 -5.776468
#> 90 3904684037 43.12507 -4.880099
#> 91 3904597803 42.90586 -8.402835
#> 92 3904516076 43.40315 -4.744980
#> 93 3904369429 43.22844 -6.046053
#> 94 3904282757 43.22840 -6.046134
#> 95 3903197158 40.08107 -8.203597
#> 96 3902606076 41.57694 -7.982271
#> 97 3902601687 41.16419 -8.482072
#> 98 3902423737 40.67403 -8.214256
#> 99 3902372729 41.25738 -7.935210
#> 100 3888804754 40.37550 -8.365012
#> 101 3873365684 40.37473 -8.365758
#> 102 3860741206 40.89950 -8.235959
#> 103 3860517442 41.28359 -7.838244
#> 104 3860325381 41.73333 -8.160636
#> 105 3860006479 39.87334 -8.852991
#> 106 3859664580 41.27729 -7.995230
#> 107 3859567137 40.37849 -8.371000
#> 108 3858854802 41.72320 -8.129511
#> 109 3858854034 40.35641 -7.558820
#> 110 3827447637 40.29260 -5.171154
#> 111 3827426685 40.30645 -5.190073
#> 114 3827170532 40.29279 -5.171848
#> 115 3827155357 43.25506 -8.213604
#> 116 3827120895 39.38935 -5.382989
#> 117 3826867567 43.29656 -8.554120
#> 118 3826866390 43.55284 -7.155949
#> 119 3826810234 43.10727 -6.259454
#> 120 3826711597 43.57161 -5.699182
#> 121 3826663742 43.49593 -5.934134
#> 123 3826194745 43.39108 -8.323385
#> 124 3826132575 40.35440 -5.112846
#> 125 3826103015 43.57041 -5.722249
#> 126 3826079371 43.55287 -7.155935
#> 127 3826034598 40.30568 -5.204373
#> 128 3825994248 43.55289 -7.156223
#> 129 3825834008 42.43518 -7.787696
#> 130 3825671626 42.29921 -8.427880
#> 131 3825286990 42.88293 -5.763342
#> 132 3825242818 43.11725 -6.284291
#> 133 3824773631 40.28159 -5.229868
#> 134 3824658562 42.75729 -8.272977
#> 135 3824458747 40.11800 -5.778082
#> 136 3824395901 43.60045 -5.920679
#> 138 3824325042 43.25876 -6.132585
#> 140 3824114271 43.55380 -7.150638
#> 141 3823724468 40.30637 -5.190222
#> 142 3823695876 42.04758 -7.833532
#> 143 3823679192 43.16100 -7.811129
#> 144 3823644579 43.22112 -8.282746
#> 145 3823558122 43.57641 -5.990616
#> 146 3823525379 42.43281 -8.395458
#> 147 3823405779 43.25557 -8.215127
#> 148 3823370594 40.30734 -5.190725
#> 149 3823352692 43.26045 -7.489225
#> 150 3823006585 43.25548 -8.215295
#> 151 3822784961 42.07946 -7.761854
#> 152 3822665898 42.90595 -5.805009
#> 153 3802554684 40.35618 -7.558206
#> 154 3802446032 41.31493 -8.257731
#> 155 3785165345 41.09905 -8.559851
#> 156 3785030262 40.44248 -7.515020
#> 157 3784779955 41.19607 -8.161694
#> 158 3773639035 42.00592 -8.166519
#> 159 3773638868 40.92789 -8.259287
#> 160 3773600023 40.32829 -7.586550
#> 161 3773579360 41.46367 -8.397842
#> 162 3773331266 41.46369 -8.397868
#> 163 3772430861 41.20036 -8.680263
#> 164 3764501931 41.28115 -8.730330
#> 165 3764237964 37.30751 -8.575900
#> 166 3760331840 40.66485 -7.906360
#> 167 3760256841 40.66477 -7.906402
#> 168 3760245302 41.80926 -8.132585
#> 169 3759917005 40.93054 -8.246717
#> 171 3759664475 41.28517 -8.340068
#> 172 3759511079 40.11704 -8.497478
#> 173 3759285347 41.31493 -8.257731
#> 174 3747177530 41.29057 -8.235037
#> 175 3747105785 41.82141 -8.295183
#> 176 3742983478 40.01487 -8.587528
#> 177 3733279230 43.54593 -8.046426
#> 178 3732795612 42.12000 -6.770000
#> 179 3732784603 43.38732 -4.323388
#> 180 3732557326 43.28899 -6.771403
#> 181 3732423322 42.43443 -7.704437
#> 182 3732260894 40.26377 -5.268384
#> 183 3731723971 43.73630 -7.703093
#> 186 3731246742 40.27246 -5.234587
#> 187 3731140171 40.27177 -5.246742
#> 188 3730867547 43.57594 -5.992134
#> 189 3730795274 43.57253 -5.994503
#> 190 3730747451 43.40228 -8.327387
#> 191 3730259342 43.30598 -8.536074
#> 192 3729886329 43.28944 -8.489956
#> 193 3729533373 43.30155 -8.503598
#> 194 3729338522 40.35877 -5.526905
#> 195 3729243652 43.59940 -5.939154
#> 196 3729232715 43.30372 -8.428818
#> 197 3729072329 40.34943 -5.295884
#> 198 3728708629 42.92490 -8.169698
#> 199 3728646722 40.27644 -5.231343
#> 200 3728584484 40.27056 -5.238148
#> 201 3728498330 43.10000 -6.270000
#> 202 3728484592 43.30411 -8.606538
#> 203 3728404536 40.21742 -7.732143
#> 204 3728122900 43.25263 -6.728374
#> 205 3728027283 40.29539 -5.173345
#> 206 3727992574 40.29203 -5.171374
#> 207 3727770407 43.04447 -6.251601
#> 208 3727675314 43.28323 -8.545810
#> 209 3727241475 40.34139 -5.186852
#> 210 3726885606 42.11812 -6.714393
#> 211 3726351000 42.03855 -6.887438
#> 212 3726280229 43.10000 -6.270000
#> 213 3726168466 43.15211 -5.600696
#> 214 3726010635 43.10000 -4.060000
#> 215 3725698669 43.13044 -4.809709
#> 216 3725422827 42.44063 -6.394794
#> 219 3725093673 40.42682 -6.149007
#> 220 3725060873 43.15699 -6.952821
#> 222 3722268573 43.55432 -6.111816
#> 224 3721736481 43.47930 -7.913536
#> 225 3721494769 43.29768 -8.570078
#> 226 3721392112 40.27242 -5.234640
#> 227 3721022126 40.51314 -6.167968
#> 228 3720913817 43.30505 -8.535715
#> 229 3720828362 43.48000 -7.050000
#> 230 3720765384 43.08638 -9.192246
#> 231 3720759073 43.44339 -5.791692
#> 232 3720503132 40.51314 -6.167968
#> 233 3720484714 43.55238 -6.014103
#> 235 3720151980 39.47717 -5.388593
#> 236 3720021627 43.13527 -4.816869
#> 237 3719941935 43.57282 -5.994296
#> 238 3719639436 43.57665 -5.990095
#> 239 3719396101 40.26263 -5.270146
#> 240 3719284704 43.28000 -5.990000
#> 241 3719224766 39.44328 -5.344995
#> 242 3719071502 40.27115 -5.235581
#> 243 3718346021 43.27167 -8.516319
#> 244 3718113343 43.10000 -6.270000
#> 245 3718042204 43.10000 -6.270000
#> 246 3717590281 43.10000 -6.270000
#> 247 3717451694 43.29755 -8.571736
#> 248 3717446813 43.29811 -8.534722
#> 249 3717173342 40.31362 -5.627051
#> 250 3716997397 42.40000 -8.490000
#> 251 3716892411 42.07009 -6.541847
#> 253 3716632135 42.07006 -6.541246
#> 254 3716268286 40.27742 -5.111580
#> 256 3715931235 43.23981 -8.901147
#> 258 3715143459 40.24000 -5.330000
#> 259 3698100690 39.31591 -7.330855
#> 260 3456534329 39.60585 -8.359525
#> 261 3415427142 41.08160 -8.471741
#> 262 3408234996 40.81437 -8.227214
#> 263 3390592007 41.81128 -8.044139
#> 264 3390592000 41.92987 -8.247404
#> 265 3390591998 41.41318 -7.846593
#> 266 3390591995 41.78309 -7.912192
#> 267 3390591971 42.00115 -8.137898
#> 268 3390591967 37.37215 -8.476313
#> 269 3390591966 41.81295 -8.272851
#> 270 3390591954 37.31002 -8.781429
#> 271 3390591946 41.65946 -8.214528
#> 272 3390591940 41.79707 -8.802613
#> 273 3390591931 41.41178 -7.715009
#> 274 3390591923 41.74814 -8.033049
#> 275 3390591909 41.29657 -7.896422
#> 276 3390591908 42.03769 -8.209891
#> 277 3390591902 41.86966 -6.525084
#> 278 3390591891 37.63410 -8.621811
#> 279 3390591866 37.35397 -8.442567
#> 280 3390591865 41.32324 -7.860128
#> 281 3390591864 41.91170 -8.223503
#> 282 3390591861 40.90137 -8.021737
#> 283 3390591856 41.93926 -8.307606
#> 284 3390591855 37.82380 -8.791282
#> 285 3390591848 41.78436 -8.056572
#> 286 3390591824 41.81945 -7.947697
#> 287 3390591815 41.85524 -7.923018
#> 288 3390591813 41.92319 -6.957134
#> 289 3390591805 41.76881 -8.429748
#> 290 3390591793 42.00124 -8.149971
#> 291 3390591787 41.76683 -8.116979
#> 292 3390591786 41.50183 -7.713232
#> 293 3390591783 41.74017 -8.165445
#> 294 3390591775 41.76674 -8.104951
#> 295 3390591774 41.88515 -8.296138
#> 296 3390591766 41.93966 -6.872173
#> 297 3390591765 41.84890 -8.260399
#> 298 3390591763 41.81022 -7.923772
#> 299 3390591761 41.90338 -8.332106
#> 300 3390591747 41.93197 -6.944793
#> 301 3390591745 41.81922 -7.923621
#> 302 3390591734 41.77685 -8.261227
#> 303 3390591720 41.81213 -8.152474
#> 304 3390591718 41.94841 -8.331636
#> 305 3390591710 41.92404 -7.005345
#> 306 3390591705 37.42686 -8.645462
#> 307 3390591681 41.76615 -8.032778
#> 308 3390591680 40.85867 -8.354572
#> 309 3390591679 41.79385 -8.116609
#> 310 3390591665 39.34446 -7.359429
#> 311 3390591654 41.77628 -8.177010
#> 312 3390591642 42.02817 -8.137533
#> 313 3390591632 41.78502 -8.140797
#> 314 3390591631 41.93447 -7.089449
#> 315 3390591625 41.80165 -7.972062
#> 316 3390591614 41.92705 -7.897690
#> 317 3390591611 41.80143 -7.947992
#> 318 3390591594 41.34952 -7.787951
#> 319 3390591593 41.87560 -8.211889
#> 320 3390591590 37.40887 -8.656846
#> 321 3390591586 40.98243 -8.020540
#> 322 3390591581 41.69460 -8.093934
#> 323 3390591574 41.75745 -8.068994
#> 324 3390591566 41.95712 -8.283281
#> 325 3390591555 41.77442 -7.948433
#> 326 3390591548 41.95775 -8.391870
#> 327 3390591547 41.36753 -7.787617
#> 328 3390591546 41.81087 -7.995991
#> 329 3390591535 41.85579 -7.983243
#> 330 3390591529 41.84646 -7.947255
#> 331 3390591521 41.84699 -8.007472
#> 332 3390591513 41.67739 -8.202297
#> 333 3390591507 41.96613 -8.283180
#> 334 3390591490 41.86642 -8.187904
#> 335 3390591486 42.02826 -8.149611
#> 336 3390591482 41.97498 -8.258943
#> 337 3390591474 41.70449 -8.213981
#> 338 3390591461 39.29008 -7.337507
#> 339 3390591422 41.74918 -8.165329
#> 340 3390591411 41.88026 -7.079013
#> 341 3390591410 41.71192 -8.009551
#> 342 3390591403 41.79265 -7.972206
#> 343 3390591393 41.89377 -8.235774
#> 344 3390591374 42.01016 -8.137777
#> 345 3390591372 39.29926 -7.348887
#> 346 3390591366 41.72250 -8.213761
#> 347 3390591343 42.00167 -8.210336
#> 348 3390591323 41.88886 -7.054650
#> 349 3390591318 41.91036 -6.752582
#> 350 3390591315 41.75725 -8.044940
#> 351 3390591312 41.75782 -8.117103
#> 352 3390591306 41.93986 -8.416165
#> 353 3390591295 41.26835 -7.777515
#> 354 3390591291 41.82919 -8.031831
#> 355 3390591283 41.92297 -6.945082
#> 356 3390591267 41.92929 -6.800157
#> 357 3390591254 40.80315 -8.129872
#> 358 3390591245 41.34077 -7.812017
#> 359 3390591231 41.77453 -7.960463
#> 360 3390591221 41.92995 -8.259464
#> 361 3390591219 41.79243 -7.948139
#> 362 3390591208 41.77602 -8.140917
#> 363 3390591205 41.83745 -7.947403
#> 364 3390591203 41.91805 -7.897845
#> 365 3390591199 41.88477 -8.235881
#> 366 3390591191 37.34516 -8.487790
#> 367 3390591167 41.93986 -8.416165
#> 368 3390591125 41.37654 -7.787450
#> 369 3390591119 41.97440 -8.174466
#> 370 3390591118 37.31886 -8.702402
#> 371 3390591116 41.81911 -7.911583
#> 372 3390591111 41.32382 -7.919859
#> 373 3390591108 41.79394 -8.128643
#> 374 3390591086 41.91793 -7.885789
#> 375 3390591085 41.85590 -7.995288
#> 376 3390591082 41.87347 -7.946812
#> 377 3390591080 41.75673 -7.984807
#> 378 3390591079 41.94204 -7.004784
#> 379 3390591076 41.83767 -7.971486
#> 380 3390591065 40.94556 -7.926045
#> 381 3390591060 41.72171 -8.105575
#> 382 3390591051 42.07328 -8.149011
#> 383 3390591043 41.81044 -7.947845
#> 384 3390591036 41.86480 -7.983100
#> 385 3390591035 41.72180 -8.117596
#> 386 3390591031 41.67801 -8.298398
#> 387 3390591011 41.91816 -7.909902
#> 388 3390591002 41.82122 -8.164394
#> 389 3390590999 41.67763 -8.238334
#> 390 3390590995 41.94747 -8.186877
#> 391 3390590988 41.69548 -8.214090
#> 392 3390590986 41.94367 -7.101237
#> 393 3390590978 41.97506 -8.271011
#> 394 3390590977 41.75801 -8.141157
#> 395 3390590976 42.01934 -8.161808
#> 396 3390590973 41.93261 -6.980956
#> 397 3390590959 41.83974 -8.236417
#> 398 3390590956 41.91545 -7.029728
#> 399 3390590947 41.78586 -8.261123
#> 400 3390590946 41.82823 -7.923470
#> 401 3390590942 41.87358 -7.958860
#> 402 3390590935 41.80237 -8.056308
#> 403 3390590924 37.34531 -8.521658
#> 404 3390590910 40.95510 -7.985299
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#> 408 3390590889 41.90004 -7.898155
#> 409 3390590874 41.88529 -8.320242
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#> 413 3390590860 41.48300 -7.641741
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#> 459 3390590552 37.33655 -8.589445
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#> 463 3390590528 37.66117 -8.633012
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#> 471 3390590476 39.31776 -7.383252
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#> 474 3390590468 37.48996 -8.645164
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#> 480 3390590416 41.91162 -8.211447
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#> 483 3390590371 42.07292 -8.100665
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#> 488 3390590335 41.73125 -8.177584
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#> 490 3390590320 37.44508 -8.713205
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#> 494 3390590304 41.80266 -8.092414
#> 495 3390590303 41.85557 -7.959153
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#> 498 3390590275 39.38900 -7.323552
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#> 503 3390590228 37.31860 -8.612115
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#> 507 3390590208 41.51957 -7.688912
#> 508 3390590207 41.76800 -8.285389
#> 509 3390590203 40.88240 -7.915197
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#> 511 3390590186 41.68605 -8.154129
#> 512 3390590185 41.82845 -7.947550
#> 513 3390590169 41.95019 -6.956272
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#> 515 3390590151 41.49242 -7.677481
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#> 518 3390590109 41.79438 -8.188814
#> 519 3390590102 41.94161 -6.980672
#> 520 3390590099 41.80247 -8.068343
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#> 522 3390590090 41.82019 -8.031967
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#> 524 3390590080 41.90113 -6.740851
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#> 528 3390590062 41.36766 -7.799571
#> 529 3390590061 41.91231 -8.319955
#> 530 3390590057 41.95040 -6.968330
#> 531 3390590042 41.00003 -7.972721
#> 532 3390590033 39.28074 -7.314540
#> 533 3390590026 41.71333 -8.189833
#> 534 3390590009 41.76664 -8.092922
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#> 536 3390590005 37.52598 -8.633677
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#> 541 3390589971 41.35037 -7.871608
#> 542 3390589968 41.82140 -8.188473
#> 543 3390589950 41.25071 -7.813649
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#> 547 3390589905 41.80075 -7.875784
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#> 551 3390589860 41.73013 -8.033318
#> 552 3390589851 39.39800 -7.323337
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#> 555 3390589838 41.92361 -6.981239
#> 556 3390589813 37.35472 -8.634512
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#> 561 3390589794 41.84589 -7.887040
#> 563 3390589783 39.20817 -7.281532
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#> 1244 2835218217 40.24479 -7.950605
#> 1245 2835202861 40.13605 -5.667826
#> 1246 2834983824 43.09492 -7.045802
#> 1247 2834817357 40.06247 -5.856696
#> 1248 2834361613 41.74000 -8.170000
#> 1249 2834285996 40.38311 -7.545340
#> 1250 2834282668 40.38310 -7.545344
#> 1251 2834210083 43.33548 -8.479428
#> 1252 2834106611 40.25759 -5.655870
#> 1253 2833589613 40.06848 -5.711360
#> 1254 2833491788 42.08339 -8.678606
#> 1255 2833359648 42.33765 -8.444519
#> 1256 2833144483 40.15827 -5.656801
#> 1257 2833038154 40.11728 -5.777557
#> 1258 2833006260 40.32750 -5.130056
#> 1259 2832994801 41.97207 -8.374844
#> 1260 2832994638 41.03139 -8.046394
#> 1261 2832984061 40.22100 -5.141100
#> 1262 2832932466 41.57798 -8.230280
#> 1263 2832927493 40.16900 -5.650477
#> 1264 2832919246 40.27000 -5.240000
#> 1265 2832795760 41.13430 -8.664462
#> 1266 2832761972 40.22096 -5.749867
#> 1267 2832685385 42.10434 -6.770668
#> 1268 2832674015 40.36523 -5.045183
#> 1269 2832601288 40.36002 -5.762501
#> 1270 2832583582 41.78928 -8.153801
#> 1271 2832582758 40.27219 -5.234327
#> 1272 2832519117 40.12036 -5.776772
#> 1273 2832517081 40.45940 -6.144000
#> 1274 2832480115 39.36000 -7.390000
#> 1275 2832400951 43.02674 -6.863365
#> 1276 2832295330 42.63374 -7.133861
#> 1277 2832293209 42.58584 -7.055038
#> 1278 2832292205 41.80000 -8.140000
#> 1279 2832282319 40.24507 -7.950164
#> 1280 2832271078 40.36735 -7.727165
#> 1281 2832258818 42.75239 -8.173088
#> 1282 2832246684 40.18118 -7.861965
#> 1283 2832217151 40.21632 -7.918836
#> 1285 2626338858 41.82110 -8.297938
#> 1286 2626301455 38.79306 -9.422247
#> 1287 2521406365 41.26347 -7.442267
#> 1288 2521405897 41.40247 -7.465518
#> 1289 2521405667 41.35846 -7.403038
#> 1290 2464748146 41.72318 -8.129463
#> 1291 2442913242 37.28064 -8.555517
#> 1292 2442871645 41.75148 -8.201509
#> 1293 1945419094 41.24880 -7.811231
#> 1294 1890067578 39.30139 -9.217903
#> 1295 1580129201 38.08485 -6.422796
#> 1296 1562900214 39.30134 -9.218001
#> 1297 1338880563 37.34785 -8.816786
Alternatively, we can easily access and manipulate this dataset using
rbgif:
# download presences
library(rgbif)
occ_download_get(key = "0068808-230530130749713", path = tempdir())
# read file
library(readr)
distrib <- read_delim(file.path(tempdir(), "0068808-230530130749713.zip"))
# keep the necessary columns and rename them
lacerta <- distrib %>% select(gbifID, decimalLatitude, decimalLongitude) %>%
rename(ID = gbifID, latitude = decimalLatitude, longitude = decimalLongitude)
Preparing your data
First, let us visualise our presences by plotting on a map.
tidysdm works with sf objects to represent
locations, so we will cast our coordinates into an sf
object, and set its projections to standard ‘lonlat’ (crs =
4326).
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
lacerta <- st_as_sf(lacerta, coords = c("longitude", "latitude"))
st_crs(lacerta) <- 4326
It is usually advisable to plot the locations directly on the raster
that will be used to extract climatic variables, to see how the
locations fall within the discrete space of the raster. For this
vignette, we will use WorldClim as our source of climatic information.
We will access the WorldClim data via the library pastclim;
even though this library, as the name suggests, is mostly designed to
handle palaeoclimatic reconstructions, it also provides convenient
functions to access present day reconstructions and future projections.
pastclim has a handy function to get the land mask for the
available datasets, which we can use as background for our locations. We
will cut the raster to the Iberian peninsula, where our lizard lives.
For this simply illustration, we will not bother to project the raster,
but an equal area projection would be desirable…
library(pastclim)
download_dataset(dataset = "WorldClim_2.1_10m")
land_mask <-
get_land_mask(time_ce = 1985, dataset = "WorldClim_2.1_10m")
# Iberia peninsula extension
iberia_poly <-
terra::vect(
"POLYGON((-9.8 43.3,-7.8 44.1,-2.0 43.7,3.6 42.5,3.8 41.5,1.3 40.8,0.3 39.5,
0.9 38.6,-0.4 37.5,-1.6 36.7,-2.3 36.3,-4.1 36.4,-4.5 36.4,-5.0 36.1,
-5.6 36.0,-6.3 36.0,-7.1 36.9,-9.5 36.6,-9.4 38.0,-10.6 38.9,-9.5 40.8,
-9.8 43.3))"
)
crs(iberia_poly) <- "lonlat"
# crop the extent
land_mask <- crop(land_mask, iberia_poly)
# and mask to the polygon
land_mask <- mask(land_mask, iberia_poly)
#> Loading required package: terra
#> terra 1.7.78
#>
#> Attaching package: 'terra'
#> The following object is masked from 'package:tidyr':
#>
#> extract
#> The following object is masked from 'package:scales':
#>
#> rescale
For plotting, we will take advantage of tidyterra, which
makes handling of terra rasters with ggplot a
breeze.
library(tidyterra)
#>
#> Attaching package: 'tidyterra'
#> The following object is masked from 'package:stats':
#>
#> filter
library(ggplot2)
ggplot() +
geom_spatraster(data = land_mask, aes(fill = land_mask_1985)) +
geom_sf(data = lacerta) + scale_fill_gradient(na.value = "transparent")
Thinning step
Now, we thin the observations to have one per cell in the raster (it
would be better if we had an equal area projection…):
set.seed(1234567)
lacerta <- thin_by_cell(lacerta, raster = land_mask)
nrow(lacerta)
#> [1] 226
ggplot() +
geom_spatraster(data = land_mask, aes(fill = land_mask_1985)) +
geom_sf(data = lacerta) + scale_fill_gradient(na.value = "transparent")
Now, we thin further to remove points that are closer than 20km.
However, note that the standard map units for a ‘lonlat’ projection are
meters. tidysdm provides a convening conversion function,
km2m(), to avoid having to write lots of zeroes):
set.seed(1234567)
lacerta_thin <- thin_by_dist(lacerta, dist_min = km2m(20))
nrow(lacerta_thin)
#> [1] 111
Let’s see what we have left of our points:
ggplot() +
geom_spatraster(data = land_mask, aes(fill = land_mask_1985)) +
geom_sf(data = lacerta_thin) + scale_fill_gradient(na.value = "transparent")
We now need to select points that represent the potential available
area for the species. There are two approaches, we can either sample the
background with sample_background(), or we can generate
pseudo-absences with sample_pseudoabs(). In this example,
we will sample the background; more specifically, we will attempt to
account for potential sampling biases by using a target group approach,
where presences from other species within the same taxonomic group are
used to condition the sampling of the background, providing information
on differential sampling of different areas within the region of
interest.
We will start by downloading records from 8 genera of
Lacertidae, covering the same geographic region of the Iberian
peninsula from GBIF https://doi.org/10.15468/dl.53js5z:
# download presences
library(rgbif)
# download file
occ_download_get(key = "0121761-240321170329656", path = tempdir())
# read file
library(readr)
backg_distrib <- readr::read_delim(file.path(tempdir(), "0121761-240321170329656.zip"))
# keep the necessary columns
lacertidae_background <- backg_distrib %>% select(gbifID, decimalLatitude, decimalLongitude) %>%
rename(ID = gbifID, latitude = decimalLatitude, longitude = decimalLongitude)
lacertidae_background <- st_as_sf(lacertidae_background, coords = c("longitude", "latitude"))
st_crs(lacertidae_background) <- 4326
We need to convert these observations into a raster whose values are
the number of records (which will be later used to determine how likely
each cell is to be used as a background point):
lacertidae_background_raster <- rasterize(lacertidae_background, land_mask, fun = "count")
plot(lacertidae_background_raster)
We can see that the sampling is far from random, with certain
locations having very large number of records. We can now sample the
background, using the ‘bias’ method to represent this heterogeneity in
sampling effort:
set.seed(1234567)
lacerta_thin <- sample_background(data = lacerta_thin, raster = lacertidae_background_raster,
n = 3 * nrow(lacerta_thin),
method = "bias",
class_label = "background",
return_pres = TRUE)
Let’s see our presences and background:
ggplot() +
geom_spatraster(data = land_mask, aes(fill = land_mask_1985)) +
geom_sf(data = lacerta_thin, aes(col = class)) + scale_fill_gradient(na.value = "transparent")
Generally, we can use pastclim to check what variables
are available for the WorldClim dataset:
climate_vars <- get_vars_for_dataset("WorldClim_2.1_10m")
We first download the dataset at the right resolution (here 10
arc-minutes):
download_dataset("WorldClim_2.1_10m")
And then create a terra SpatRaster object.
The dataset covers the period 1970-2000, so pastclim dates
it as 1985 (the midpoint). We can directly crop to the Iberian
peninsula:
climate_present <- pastclim::region_slice(
time_ce = 1985,
bio_variables = climate_vars,
data = "WorldClim_2.1_10m",
crop = iberia_poly
)
Next, we extract climate for all presences and background points:
lacerta_thin <- lacerta_thin %>%
bind_cols(terra::extract(climate_present, lacerta_thin, ID = FALSE))
Based on this paper (https://doi.org/10.1007/s10531-010-9865-2), we are
interested in these variables: “bio06”, “bio05”, “bio13”, “bio14”,
“bio15”. We can visualise the differences between presences and the
background using violin plots:
lacerta_thin %>% plot_pres_vs_bg(class)
We can see that all the variables of interest do seem to have a
different distribution between presences and the background. We can
formally quantify the mismatch between the two by computing the
overlap:
lacerta_thin %>% dist_pres_vs_bg(class)
#> bio09 bio12 bio16 bio13 bio05 bio10 bio19
#> 0.44341125 0.43673315 0.42163656 0.41676947 0.41107299 0.40554870 0.40009102
#> bio02 bio07 bio04 bio08 bio17 bio18 bio14
#> 0.36398134 0.34354633 0.31492272 0.30408833 0.30393285 0.27604384 0.26619609
#> bio01 bio15 bio03 bio11 altitude bio06
#> 0.26516698 0.24779818 0.15863624 0.10530412 0.09195507 0.04780224
Again, we can see that the variables of interest seem good candidates
with a clear signal. Let us then focus on those variables:
suggested_vars <- c("bio06", "bio05", "bio13", "bio14", "bio15")
Environmental variables are often highly correlated, and collinearity
is an issue for several types of models. We can inspect the correlation
among variables with:
pairs(climate_present[[suggested_vars]])
We can see that some variables have rather high correlation
(e.g. bio05 vs bio14). We can subset to variables below a certain
threshold correlation (e.g. 0.7) with:
climate_present <- climate_present[[suggested_vars]]
vars_uncor <- filter_collinear(climate_present, cutoff = 0.7, method = "cor_caret")
vars_uncor
#> [1] "bio15" "bio05" "bio13" "bio06"
#> attr(,"to_remove")
#> [1] "bio14"
So, removing bio14 leaves us with a set of uncorrelated variables.
Note that filter_collinear has other methods based on
variable inflation that would also be worth exploring. For this example,
we will remove bio14 and work with the remaining variables.
lacerta_thin <- lacerta_thin %>% select(all_of(c(vars_uncor, "class")))
climate_present <- climate_present[[vars_uncor]]
names(climate_present) # added to highlight which variables are retained in the end
#> [1] "bio15" "bio05" "bio13" "bio06"
Fit the model by cross-validation
Next, we need to set up a recipe to define how to handle
our dataset. We don’t want to do anything to our data in terms of
transformations, so we just need to define the formula (class
is the outcome, all other variables are
predictors; note that, for sf objects,
geometry is automatically replaced by X and
Y columns which are assigned a role of coords,
and thus not used as predictors):
lacerta_rec <- recipe(lacerta_thin, formula = class ~ .)
lacerta_rec
#>
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#>
#> ── Inputs
#> Number of variables by role
#> outcome: 1
#> predictor: 4
#> coords: 2
In classification models for tidymodels, the assumption
is that the level of interest for the response (in our case, presences)
is the reference level. We can confirm that we have the data correctly
formatted with:
lacerta_thin %>% check_sdm_presence(class)
#> [1] TRUE
We now build a workflow_set of different models,
defining which hyperparameters we want to tune. We will use
glm, random forest, boosted_trees and
maxent as our models (for more details on how to use
workflow_sets, see this
tutorial). The latter three models have tunable hyperparameters. For
the most commonly used models, tidysdm automatically
chooses the most important parameters, but it is possible to fully
customise model specifications (e.g. see the help for
sdm_spec_rf).
lacerta_models <-
# create the workflow_set
workflow_set(
preproc = list(default = lacerta_rec),
models = list(
# the standard glm specs
glm = sdm_spec_glm(),
# rf specs with tuning
rf = sdm_spec_rf(),
# boosted tree model (gbm) specs with tuning
gbm = sdm_spec_boost_tree(),
# maxent specs with tuning
maxent = sdm_spec_maxent()
),
# make all combinations of preproc and models,
cross = TRUE
) %>%
# tweak controls to store information needed later to create the ensemble
option_add(control = control_ensemble_grid())
We now want to set up a spatial block cross-validation scheme to tune
and assess our models. We will split the data by creating 3 folds. We
use the spatial_block_cv function from the package
spatialsample. spatialsample offers a number
of sampling approaches for spatial data; it is also possible to convert
objects created with blockCV (which offers further features
for spatial sampling, such as stratified sampling) into an
rsample object suitable to tisysdm with the
function blockcv2rsample.
library(tidysdm)
set.seed(100)
#lacerta_cv <- spatial_block_cv(lacerta_thin, v = 5)
lacerta_cv <- spatial_block_cv(data = lacerta_thin, v = 3, n = 5)
autoplot(lacerta_cv)
We can now use the block CV folds to tune and assess the models (to
keep computations fast, we will only explore 3 combination of
hyperparameters per model; this is far too little in real life!):
set.seed(1234567)
lacerta_models <-
lacerta_models %>%
workflow_map("tune_grid",
resamples = lacerta_cv, grid = 3,
metrics = sdm_metric_set(), verbose = TRUE
)
#> i No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 4 resampling: default_glm
#> ✔ 1 of 4 resampling: default_glm (240ms)
#> i 2 of 4 tuning: default_rf
#> i Creating pre-processing data to finalize unknown parameter: mtry
#> ✔ 2 of 4 tuning: default_rf (924ms)
#> i 3 of 4 tuning: default_gbm
#> i Creating pre-processing data to finalize unknown parameter: mtry
#> ✔ 3 of 4 tuning: default_gbm (5.4s)
#> i 4 of 4 tuning: default_maxent
#> ✔ 4 of 4 tuning: default_maxent (1.6s)
Note that workflow_set correctly detects that we have no
tuning parameters for glm. We can have a look at the
performance of our models with:
Now let’s create an ensemble, selecting the best set of parameters
for each model (this is really only relevant for the random forest, as
there were not hype-parameters to tune for the glm and
gam). We will use the Boyce continuous index as our metric to
choose the best random forest and boosted tree. When adding members to
an ensemble, they are automatically fitted to the full training dataset,
and so ready to make predictions.
lacerta_ensemble <- simple_ensemble() %>%
add_member(lacerta_models, metric = "boyce_cont")
lacerta_ensemble
#> A simple_ensemble of models
#>
#> Members:
#> • default_glm
#> • default_rf
#> • default_gbm
#> • default_maxent
#>
#> Available metrics:
#> • boyce_cont
#> • roc_auc
#> • tss_max
#>
#> Metric used to tune workflows:
#> • boyce_cont
And visualise it
autoplot(lacerta_ensemble)
A tabular form of the model metrics can be obtained with:
lacerta_ensemble %>% collect_metrics()
#> # A tibble: 12 × 5
#> wflow_id .metric mean std_err n
#> <chr> <chr> <dbl> <dbl> <int>
#> 1 default_glm boyce_cont 0.547 0.127 3
#> 2 default_glm roc_auc 0.773 0.0349 3
#> 3 default_glm tss_max 0.507 0.0430 3
#> 4 default_rf boyce_cont 0.722 0.0989 3
#> 5 default_rf roc_auc 0.771 0.00988 3
#> 6 default_rf tss_max 0.472 0.0467 3
#> 7 default_gbm boyce_cont 0.661 0.129 3
#> 8 default_gbm roc_auc 0.788 0.00224 3
#> 9 default_gbm tss_max 0.514 0.0135 3
#> 10 default_maxent boyce_cont 0.751 0.101 3
#> 11 default_maxent roc_auc 0.798 0.0198 3
#> 12 default_maxent tss_max 0.554 0.0186 3
Projecting to the present
We can now make predictions with this ensemble (using the default
option of taking the mean of the predictions from each model).
prediction_present <- predict_raster(lacerta_ensemble, climate_present)
ggplot() +
geom_spatraster(data = prediction_present, aes(fill = mean)) +
scale_fill_terrain_c() +
# plot presences used in the model
geom_sf(data = lacerta_thin %>% filter(class == "presence"))
We can subset the ensemble to only use the best models, based on the
Boyce continuous index, by setting a minimum threshold of 0.7 for that
metric. We will also take the median of the available model predictions
(instead of the mean, which is the default). The plot does not change
much (the models are quite consistent).
prediction_present_boyce <- predict_raster(lacerta_ensemble, climate_present,
metric_thresh = c("boyce_cont", 0.7),
fun = "median"
)
ggplot() +
geom_spatraster(data = prediction_present_boyce, aes(fill = median)) +
scale_fill_terrain_c() +
geom_sf(data = lacerta_thin %>% filter(class == "presence"))
Sometimes, it is desirable to have binary predictions (presence vs
absence), rather than the probability of occurrence. To do so, we first
need to calibrate the threshold used to convert probabilities into
classes (in this case, we optimise the TSS):
lacerta_ensemble <- calib_class_thresh(lacerta_ensemble,
class_thresh = "tss_max",
metric_thresh = c("boyce_cont", 0.7)
)
And now we can predict for the whole continent:
prediction_present_binary <- predict_raster(lacerta_ensemble,
climate_present,
type = "class",
class_thresh = c("tss_max"),
metric_thresh = c("boyce_cont", 0.7)
)
ggplot() +
geom_spatraster(data = prediction_present_binary, aes(fill = binary_mean)) +
geom_sf(data = lacerta_thin %>% filter(class == "presence"))
Projecting to the future
WorldClim has a wide selection of projections for the future based on
different models and Shared Socio-economic Pathways (SSP). Type
help("WorldClim_2.1") for a full list. We will use
predictions based on “HadGEM3-GC31-LL” model for SSP 245 (intermediate
green house gas emissions) at the same resolution as the present day
data (10 arc-minutes). We first download the data:
download_dataset("WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m")
Let’s see what times are available:
get_time_ce_steps("WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m")
#> [1] 2030 2050 2070 2090
We will predict for 2090, the further prediction in the future that
is available.
Let’s now check the available variables:
get_vars_for_dataset("WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m")
#> [1] "bio01" "bio02" "bio03" "bio04" "bio05" "bio06" "bio07" "bio08" "bio09"
#> [10] "bio10" "bio11" "bio12" "bio13" "bio14" "bio15" "bio16" "bio17" "bio18"
#> [19] "bio19"
Note that future predictions do not include altitude (as
that does not change with time), so if we needed it, we would have to
copy it over from the present. However, it is not in our set of
uncorrelated variables that we used earlier, so we don’t need to worry
about it.
climate_future <- pastclim::region_slice(
time_ce = 2090,
bio_variables = vars_uncor,
data = "WorldClim_2.1_HadGEM3-GC31-LL_ssp245_10m",
crop = iberia_poly
)
And predict using the ensemble:
prediction_future <- predict_raster(lacerta_ensemble, climate_future)
ggplot() +
geom_spatraster(data = prediction_future, aes(fill = mean)) +
scale_fill_terrain_c()
Visualising the contribution of individual variables
It is sometimes of interest to understand the relative contribution
of individual variables to the prediction. This is a complex task,
especially if there are interactions among variables. For simpler linear
models, it is possible to obtain marginal response curves (which show
the effect of a variable whilst keeping all other variables to their
mean) using step_profile() from the recipes
package. We use step_profile() to define a new recipe which
we can then bake to generate the appropriate dataset to make the
marginal prediction. We can then plot the predictions against the values
of the variable of interest. For example, to investigate the
contribution of bio05, we would:
bio05_prof <- lacerta_rec %>%
step_profile(-bio05, profile = vars(bio05)) %>%
prep(training = lacerta_thin)
bio05_data <- bake(bio05_prof, new_data = NULL)
bio05_data <- bio05_data %>%
mutate(
pred = predict(lacerta_ensemble, bio05_data)$mean
)
ggplot(bio05_data, aes(x = bio05, y = pred)) +
geom_point(alpha = .5, cex = 1)
It is also possible to use DALEX,to explore
tidysdm models; see more details in the tidymodels
additions article.
Repeated ensembles
The steps of thinning and sampling pseudo-absences can have a bit
impact on the performance of SDMs. As these steps are stochastic, it is
good practice to explore their effect by repeating them, and then
creating ensembles of models over these repeats. In
tidysdm, it is possible to create
repeat_ensembles. We start by creating a list of
simple_ensembles, by looping through the SDM pipeline. We
will just use two fast models to speed up the process.
# empty object to store the simple ensembles that we will create
ensemble_list <- list()
set.seed(123) # make sure you set the seed OUTSIDE the loop
for (i_repeat in 1:3) {
# thin the data
lacerta_thin_rep <- thin_by_cell(lacerta, raster = climate_present)
lacerta_thin_rep <- thin_by_dist(lacerta_thin_rep, dist_min = 20000)
# sample pseudo-absences
lacerta_thin_rep <- sample_pseudoabs(lacerta_thin_rep,
n = 3 * nrow(lacerta_thin_rep),
raster = climate_present,
method = c("dist_min", 50000)
)
# get climate
lacerta_thin_rep <- lacerta_thin_rep %>%
bind_cols(terra::extract(climate_present, lacerta_thin_rep, ID = FALSE))
# create folds
lacerta_thin_rep_cv <- spatial_block_cv(lacerta_thin_rep, v = 5)
# create a recipe
lacerta_thin_rep_rec <- recipe(lacerta_thin_rep, formula = class ~ .)
# create a workflow_set
lacerta_thin_rep_models <-
# create the workflow_set
workflow_set(
preproc = list(default = lacerta_thin_rep_rec),
models = list(
# the standard glm specs
glm = sdm_spec_glm(),
# maxent specs with tuning
maxent = sdm_spec_maxent()
),
# make all combinations of preproc and models,
cross = TRUE
) %>%
# tweak controls to store information needed later to create the ensemble
option_add(control = control_ensemble_grid())
# train the model
lacerta_thin_rep_models <-
lacerta_thin_rep_models %>%
workflow_map("tune_grid",
resamples = lacerta_thin_rep_cv, grid = 10,
metrics = sdm_metric_set(), verbose = TRUE
)
# make an simple ensemble and add it to the list
ensemble_list[[i_repeat]] <- simple_ensemble() %>%
add_member(lacerta_thin_rep_models, metric = "boyce_cont")
}
#> i No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#> ✔ 1 of 2 resampling: default_glm (277ms)
#> i 2 of 2 tuning: default_maxent
#> ✔ 2 of 2 tuning: default_maxent (8.9s)
#> i No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#> ✔ 1 of 2 resampling: default_glm (303ms)
#> i 2 of 2 tuning: default_maxent
#> ✔ 2 of 2 tuning: default_maxent (9.5s)
#> i No tuning parameters. `fit_resamples()` will be attempted
#> i 1 of 2 resampling: default_glm
#> ✔ 1 of 2 resampling: default_glm (272ms)
#> i 2 of 2 tuning: default_maxent
#> ✔ 2 of 2 tuning: default_maxent (9.6s)
Now we can create a repeat_ensemble from the list:
lacerta_rep_ens <- repeat_ensemble() %>% add_repeat(ensemble_list)
lacerta_rep_ens
#> A repeat_ensemble of models
#>
#> Number of repeats:
#> • 3
#>
#> Members:
#> • default_glm
#> • default_maxent
#>
#> Available metrics:
#> • boyce_cont
#> • roc_auc
#> • tss_max
#>
#> Metric used to tune workflows:
#> • boyce_cont
We can summarise the goodness of fit of models for each repeat with
collect_metrics(), but there is no autoplot()
function for repeated_ensemble objects.
We can then predict in the usual way (we will take the mean and
median of all models):
lacerta_rep_ens <- predict_raster(lacerta_rep_ens, climate_present,
fun = c("mean", "median")
)
ggplot() +
geom_spatraster(data = lacerta_rep_ens, aes(fill = median)) +
scale_fill_terrain_c()
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
