library(knitr)
library(dismo)
library(gbm)
library(randomForest)
library(ranger) # for the Random Forest
library(terra)
terraOptions(tempdir='big_data/temp')
library(sf)
library(tidyverse)Leopardus wiedii Variable Selection
Variable Selection for Leopardus wiedii, using the data generated in the previous step.
- R Libraries
28 Predictors
bio1: Annual Mean Temperature, from Bioclimatic variables (WorldClim V2.1)bio2: Mean Diurnal Range (Mean of monthly (max temp - min temp)), from Bioclimatic variables (WorldClim V2.1)bio3: Isothermality (BIO2/BIO7) (×100), from Bioclimatic variables (WorldClim V2.1)bio4: Temperature Seasonality (standard deviation ×100), from Bioclimatic variables (WorldClim V2.1)bio5: Max Temperature of Warmest Month, from Bioclimatic variables (WorldClim V2.1)bio6: Min Temperature of Coldest Month, from Bioclimatic variables (WorldClim V2.1)bio7: Temperature Annual Range (BIO5-BIO6), from Bioclimatic variables (WorldClim V2.1)bio8: Mean Temperature of Wettest Quarter, from Bioclimatic variables (WorldClim V2.1)bio9: Mean Temperature of Driest Quarter, from Bioclimatic variables (WorldClim V2.1)bio10: Mean Temperature of Warmest Quarter, from Bioclimatic variables (WorldClim V2.1)bio11: Mean Temperature of Coldest Quarter, from Bioclimatic variables (WorldClim V2.1)bio12: Annual Precipitation, from Bioclimatic variables (WorldClim V2.1)bio13: Precipitation of Wettest Month, from Bioclimatic variables (WorldClim V2.1)bio14: Precipitation of Driest Month, from Bioclimatic variables (WorldClim V2.1)bio15: Precipitation Seasonality (Coefficient of Variation), from Bioclimatic variables (WorldClim V2.1)bio16: Precipitation of Wettest Quarter, from Bioclimatic variables (WorldClim V2.1)bio17: Precipitation of Driest Quarter, from Bioclimatic variables (WorldClim V2.1)bio18: Precipitation of Warmest Quarter, from Bioclimatic variables (WorldClim V2.1)bio19: Precipitation of Coldest Quarter, from Bioclimatic variables (WorldClim V2.1)elev: Elevation (WorldClim V2.1 SRTM elevation data), from Bioclimatic variables (WorldClim V2.1)urban: Urban and Built-up Lands, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))barren: Barren, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))water: Water Bodies, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))savanna: Savannas, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))woodysavanna: Woody savannas, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))wetland: Permanent Wetlands, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))grass: Grasslands, from Land cover LC1 (MODIS TERRA LandCover_Type_Yearly_500m (MCD12Q1))npp: Net Primary Production (NPP) (MODIS TERRA Net_PP_GapFil_Yearly_500m (M*D17A3HGF))tree: Percentage of Tree Cover, from Vegetation Continuous Fields (MODIS TERRA Veg_Cont_Fields_Yearly_250m (MOD44B))nontree: Percentage of No Tree Cover, from Vegetation Continuous Fields (MODIS TERRA Veg_Cont_Fields_Yearly_250m (MOD44B))nonveg: Percentage of Non Tree Vegetation Cover, from Vegetation Continuous Fields (MODIS TERRA Veg_Cont_Fields_Yearly_250m (MOD44B))
Code
bio <- rast('big_data/bio_high.tif')
elev <- rast('big_data/elev_high.tif')
urban <- rast('big_data/urban_high.tif') %>% resample(., elev)
barren <- rast('big_data/barren_high.tif') %>% resample(., elev)
water <- rast('big_data/water_high.tif') %>% resample(., elev)
savanna <- rast('big_data/savanna_high.tif') %>% resample(., elev)
woodysavanna <- rast('big_data/woodysavanna_high.tif') %>% resample(., elev)
wetland <- rast('big_data/wetland_high.tif') %>% resample(., elev)
grass <- rast('big_data/grass_high.tif') %>% resample(., elev)
npp <- rast('big_data/npp_high.tif') %>% resample(., elev)
names(npp) <- 'npp'
tree <- rast('big_data/tree_high.tif') %>% resample(., elev)
nontree <- rast('big_data/nontree_high.tif') %>% resample(., elev)
nonveg <- rast('big_data/nonveg_high.tif') %>% resample(., elev)
env <- c(bio, elev, urban, barren, water,
savanna, woodysavanna, wetland,
grass, npp, tree, nontree, nonveg) %>% scale()
rm(bio, elev, urban, barren, water,
savanna, woodysavanna, wetland,
grass, npp, tree, nontree, nonveg)
gc()Leopardus wiedii’ preferences
We will use the data from both periods
Code
PA_time1 <- readRDS('data/species_POPA_data/PA_lwiedii_time1_blobs.rds')%>% ungroup()
PA_time2 <- readRDS('data/species_POPA_data/PA_lwiedii_time2_blobs.rds')%>% ungroup()
PA_time1 %>% st_drop_geometry() %>% head() %>% kable()| ID | presence | temporalSpan | effort | blobArea |
|---|---|---|---|---|
| 1 | 0 | 1365 days | 1043 | 241873943 [m^2] |
| 2 | 1 | 1769 days | 2404 | 537081878 [m^2] |
| 3 | 1 | 6222 days | 11078 | 2241827859 [m^2] |
| 4 | 1 | 720 days | 1291 | 162791738 [m^2] |
| 5 | 1 | 1769 days | 1779 | 756798182 [m^2] |
| 6 | 0 | 304 days | 3300 | 19990863 [m^2] |
Code
PA_time2 %>% st_drop_geometry() %>% head() %>% kable()| ID | presence | temporalSpan | effort | blobArea |
|---|---|---|---|---|
| 1 | 1 | 510 days | 11095 | 2.229446e+09 [m^2] |
| 2 | 1 | 150 days | 12276 | 1.542295e+03 [m^2] |
| 3 | 1 | 407 days | 1585 | 1.049520e-01 [m^2] |
| 4 | 1 | 869 days | 1440 | 7.494574e+01 [m^2] |
| 5 | 0 | 70 days | 400 | 2.546836e+01 [m^2] |
| 6 | 1 | 756 days | 7164 | 2.398904e-01 [m^2] |
Presence-absence data for the second period
Preparation of data for the tests
Code
# combine pre and pos datasets
PA.data <- st_join(PA_time1, PA_time2 %>% dplyr::select(presence), left = T) %>%
group_by(ID) %>%
mutate(presence=max(presence.x, presence.y, na.rm = T))
# calculate area, coordinates, and extract env predictors for each blob
PA.coords <- st_coordinates(st_centroid(PA.data)) %>% as_tibble()
PA.area <- as.numeric(PA.data$blobArea)
PA.env <- terra::extract(x = env, y = vect(PA.data),
fun = mean, rm.na=T) %>%
mutate(across(where(is.numeric), ~ifelse(is.nan(.), NA, .)))
## the data
PA <- data.frame(PA.coords,
area = PA.area,
presabs = PA.data$presence,
env = PA.env)Correlation between variables
Code
PA %>% filter(!if_any(everything(), is.na)) %>% dplyr::select(-c(1:5)) %>% cor() %>% kable()| env.bio_1 | env.bio_2 | env.bio_3 | env.bio_4 | env.bio_5 | env.bio_6 | env.bio_7 | env.bio_8 | env.bio_9 | env.bio_10 | env.bio_11 | env.bio_12 | env.bio_13 | env.bio_14 | env.bio_15 | env.bio_16 | env.bio_17 | env.bio_18 | env.bio_19 | env.elev | env.urban | env.barren | env.water | env.savanna | env.woodysavanna | env.wetland | env.grass | env.npp | env.tree | env.nontree | env.nonveg | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| env.bio_1 | 1.0000000 | 0.9296441 | 0.9683961 | 0.2245524 | 0.3650713 | -0.0974913 | 0.2598362 | 0.4133726 | -0.1088920 | -0.2171595 | 0.1956382 | -0.2049978 | 0.6915905 | -0.6813646 | 0.8361199 | 0.9132283 | -0.5533539 | 0.8696582 | 0.9064849 | -0.6579087 | -0.2201133 | -0.0977410 | 0.2893823 | -0.0024650 | 0.0280840 | 0.1505896 | -0.1029622 | -0.5423448 | 0.2241057 | 0.1944363 | 0.3216861 |
| env.bio_2 | 0.9296441 | 1.0000000 | 0.8201186 | 0.1070940 | 0.1263286 | -0.0188141 | 0.1075105 | 0.1704404 | -0.0270968 | -0.2734955 | 0.1868085 | -0.1426003 | 0.4381869 | -0.3771548 | 0.9194218 | 0.7840670 | -0.3309997 | 0.8667005 | 0.7893311 | -0.8088566 | -0.2416197 | -0.1129521 | 0.2673516 | 0.0269795 | 0.0437261 | 0.2158389 | -0.1911302 | -0.5957826 | 0.2394162 | 0.2026016 | 0.3128536 |
| env.bio_3 | 0.9683961 | 0.8201186 | 1.0000000 | 0.3145337 | 0.4841739 | -0.0902739 | 0.3019023 | 0.5416489 | -0.1019188 | -0.2194668 | 0.2423853 | -0.2688312 | 0.8182906 | -0.8385889 | 0.7284579 | 0.9531144 | -0.6805953 | 0.7863656 | 0.9351986 | -0.5387853 | -0.1778445 | -0.0766928 | 0.3146130 | -0.0212454 | 0.0151050 | 0.0830030 | -0.0941385 | -0.4287304 | 0.2510269 | 0.1763470 | 0.3326884 |
| env.bio_4 | 0.2245524 | 0.1070940 | 0.3145337 | 1.0000000 | 0.7642354 | 0.7267723 | -0.4858212 | 0.7801517 | 0.7257054 | 0.3332077 | 0.7446606 | -0.5595604 | 0.5005643 | -0.4148608 | -0.0696912 | 0.4462757 | -0.6123342 | 0.0898132 | 0.3375557 | -0.2702648 | 0.0226405 | -0.1373670 | 0.3077364 | -0.4152737 | -0.1495779 | -0.1146293 | -0.1512988 | 0.2195306 | 0.5598284 | -0.0677038 | 0.2675771 |
| env.bio_5 | 0.3650713 | 0.1263286 | 0.4841739 | 0.7642354 | 1.0000000 | 0.1933128 | 0.1171199 | 0.9784670 | 0.1835373 | 0.3598166 | 0.4346112 | -0.3516015 | 0.6733107 | -0.6659381 | -0.0034808 | 0.5031871 | -0.6367356 | 0.2330854 | 0.4266622 | -0.0644255 | 0.0457209 | -0.1574742 | 0.2459976 | -0.3587865 | -0.0619659 | -0.1330635 | 0.1157962 | 0.0741961 | 0.3196852 | 0.0073694 | 0.2150625 |
| env.bio_6 | -0.0974913 | -0.0188141 | -0.0902739 | 0.7267723 | 0.1933128 | 1.0000000 | -0.8648488 | 0.1913458 | 0.9965494 | 0.1837194 | 0.7740640 | -0.5300579 | 0.0892696 | 0.1186764 | -0.1996677 | 0.1249108 | -0.3001855 | -0.1378122 | 0.0099549 | -0.3037383 | -0.0295608 | -0.0459756 | 0.2895092 | -0.3454885 | -0.1352915 | -0.0004524 | -0.3242425 | 0.2813411 | 0.5902548 | -0.0208942 | 0.2560326 |
| env.bio_7 | 0.2598362 | 0.1075105 | 0.3019023 | -0.4858212 | 0.1171199 | -0.8648488 | 1.0000000 | 0.1220172 | -0.8782263 | -0.1345391 | -0.5756748 | 0.4185941 | 0.2440087 | -0.3840589 | 0.2496259 | 0.1033349 | 0.0482055 | 0.2443413 | 0.1945389 | 0.2985608 | 0.0225795 | -0.0051382 | -0.1606119 | 0.2264854 | 0.1850820 | -0.0391527 | 0.2617882 | -0.2260104 | -0.4273476 | 0.0504557 | -0.1488980 |
| env.bio_8 | 0.4133726 | 0.1704404 | 0.5416489 | 0.7801517 | 0.9784670 | 0.1913458 | 0.1220172 | 1.0000000 | 0.1819016 | 0.3180265 | 0.4533295 | -0.4161798 | 0.7057661 | -0.7191324 | 0.0339333 | 0.5715997 | -0.6962200 | 0.2568050 | 0.4948495 | -0.1003456 | 0.0642175 | -0.1537781 | 0.2693363 | -0.3170436 | -0.0375880 | -0.1611847 | 0.0346935 | 0.0835534 | 0.3395489 | -0.0039337 | 0.2253595 |
| env.bio_9 | -0.1088920 | -0.0270968 | -0.1019188 | 0.7257054 | 0.1835373 | 0.9965494 | -0.8782263 | 0.1819016 | 1.0000000 | 0.1840244 | 0.7748265 | -0.5272262 | 0.0770351 | 0.1295226 | -0.2067214 | 0.1137978 | -0.2912206 | -0.1496451 | -0.0013905 | -0.3043808 | -0.0294760 | -0.0483047 | 0.2836730 | -0.3479909 | -0.1389403 | -0.0005642 | -0.3307918 | 0.2925463 | 0.5897787 | -0.0290542 | 0.2499643 |
| env.bio_10 | -0.2171595 | -0.2734955 | -0.2194668 | 0.3332077 | 0.3598166 | 0.1837194 | -0.1345391 | 0.3180265 | 0.1840244 | 1.0000000 | -0.1092807 | -0.1343382 | -0.0811383 | 0.1021154 | -0.4429341 | -0.1997582 | -0.0648619 | -0.0145843 | -0.3385074 | 0.1685297 | 0.2582501 | -0.1263971 | -0.1456711 | -0.2570984 | 0.0182359 | -0.0638922 | 0.0956723 | 0.2583383 | -0.0288362 | -0.1901205 | -0.1873301 |
| env.bio_11 | 0.1956382 | 0.1868085 | 0.2423853 | 0.7446606 | 0.4346112 | 0.7740640 | -0.5756748 | 0.4533295 | 0.7748265 | -0.1092807 | 1.0000000 | -0.6357030 | 0.4318087 | -0.2273811 | -0.0001334 | 0.4547914 | -0.5733417 | 0.0224191 | 0.3619071 | -0.3934250 | -0.0896497 | -0.0345635 | 0.4128212 | -0.3398411 | -0.0967487 | -0.0340330 | -0.2794210 | 0.1254955 | 0.6191653 | 0.0444401 | 0.3673542 |
| env.bio_12 | -0.2049978 | -0.1426003 | -0.2688312 | -0.5595604 | -0.3516015 | -0.5300579 | 0.4185941 | -0.4161798 | -0.5272262 | -0.1343382 | -0.6357030 | 1.0000000 | -0.3325137 | 0.3049036 | 0.1966475 | -0.5250598 | 0.8023981 | -0.0861157 | -0.3309889 | 0.3821173 | -0.0890791 | 0.0167159 | -0.4386322 | 0.3434812 | 0.0467406 | -0.0727980 | 0.3469681 | -0.1737652 | -0.5951082 | -0.0668483 | -0.3700583 |
| env.bio_13 | 0.6915905 | 0.4381869 | 0.8182906 | 0.5005643 | 0.6733107 | 0.0892696 | 0.2440087 | 0.7057661 | 0.0770351 | -0.0811383 | 0.4318087 | -0.3325137 | 1.0000000 | -0.9118016 | 0.3261453 | 0.8255327 | -0.8073928 | 0.4789765 | 0.7641805 | -0.2005044 | -0.1124490 | -0.0021708 | 0.3366226 | -0.1551412 | -0.0112436 | -0.0695311 | -0.0158723 | -0.1472418 | 0.2873553 | 0.1459596 | 0.3206495 |
| env.bio_14 | -0.6813646 | -0.3771548 | -0.8385889 | -0.4148608 | -0.6659381 | 0.1186764 | -0.3840589 | -0.7191324 | 0.1295226 | 0.1021154 | -0.2273811 | 0.3049036 | -0.9118016 | 1.0000000 | -0.3065325 | -0.8003439 | 0.7896644 | -0.4447706 | -0.7650724 | 0.1075528 | 0.0491570 | 0.0151001 | -0.2590556 | 0.0693126 | 0.0098816 | 0.0774311 | -0.0168947 | 0.1143688 | -0.1926304 | -0.0852376 | -0.2445389 |
| env.bio_15 | 0.8361199 | 0.9194218 | 0.7284579 | -0.0696912 | -0.0034808 | -0.1996677 | 0.2496259 | 0.0339333 | -0.2067214 | -0.4429341 | -0.0001334 | 0.1966475 | 0.3261453 | -0.3065325 | 1.0000000 | 0.6102757 | -0.0543381 | 0.7628378 | 0.7072778 | -0.6630353 | -0.2740025 | -0.1002805 | 0.1226871 | 0.1672372 | 0.0462821 | 0.1653903 | -0.0906072 | -0.6102193 | 0.0503882 | 0.1630034 | 0.1918470 |
| env.bio_16 | 0.9132283 | 0.7840670 | 0.9531144 | 0.4462757 | 0.5031871 | 0.1249108 | 0.1033349 | 0.5715997 | 0.1137978 | -0.1997582 | 0.4547914 | -0.5250598 | 0.8255327 | -0.8003439 | 0.6102757 | 1.0000000 | -0.8241800 | 0.7110844 | 0.9320639 | -0.6213394 | -0.1491519 | -0.0630686 | 0.4173645 | -0.1202489 | 0.0029894 | 0.1034344 | -0.2159955 | -0.3303117 | 0.4202314 | 0.1793595 | 0.4141109 |
| env.bio_17 | -0.5533539 | -0.3309997 | -0.6805953 | -0.6123342 | -0.6367356 | -0.3001855 | 0.0482055 | -0.6962200 | -0.2912206 | -0.0648619 | -0.5733417 | 0.8023981 | -0.8073928 | 0.7896644 | -0.0543381 | -0.8241800 | 1.0000000 | -0.3509483 | -0.6692034 | 0.3091761 | -0.0078816 | 0.0078058 | -0.4383634 | 0.2711251 | 0.0293186 | -0.0121391 | 0.2074795 | -0.0198950 | -0.4936629 | -0.1095460 | -0.3848206 |
| env.bio_18 | 0.8696582 | 0.8667005 | 0.7863656 | 0.0898132 | 0.2330854 | -0.1378122 | 0.2443413 | 0.2568050 | -0.1496451 | -0.0145843 | 0.0224191 | -0.0861157 | 0.4789765 | -0.4447706 | 0.7628378 | 0.7110844 | -0.3509483 | 1.0000000 | 0.6106291 | -0.5930138 | -0.1337635 | -0.0844168 | 0.1904426 | -0.0413308 | 0.0667723 | 0.1977084 | -0.0870639 | -0.5298813 | 0.1649284 | 0.1344020 | 0.2359080 |
| env.bio_19 | 0.9064849 | 0.7893311 | 0.9351986 | 0.3375557 | 0.4266622 | 0.0099549 | 0.1945389 | 0.4948495 | -0.0013905 | -0.3385074 | 0.3619071 | -0.3309889 | 0.7641805 | -0.7650724 | 0.7072778 | 0.9320639 | -0.6692034 | 0.6106291 | 1.0000000 | -0.5894832 | -0.2207150 | -0.0580746 | 0.3417830 | 0.0150607 | 0.0015716 | 0.0673108 | -0.1438877 | -0.4089540 | 0.2748842 | 0.1941162 | 0.3490410 |
| env.elev | -0.6579087 | -0.8088566 | -0.5387853 | -0.2702648 | -0.0644255 | -0.3037383 | 0.2985608 | -0.1003456 | -0.3043808 | 0.1685297 | -0.3934250 | 0.3821173 | -0.2005044 | 0.1075528 | -0.6630353 | -0.6213394 | 0.3091761 | -0.5930138 | -0.5894832 | 1.0000000 | 0.1776808 | 0.0973969 | -0.2664306 | 0.0973135 | 0.0062860 | -0.1804121 | 0.3347777 | 0.3623028 | -0.3968661 | -0.0874815 | -0.3009313 |
| env.urban | -0.2201133 | -0.2416197 | -0.1778445 | 0.0226405 | 0.0457209 | -0.0295608 | 0.0225795 | 0.0642175 | -0.0294760 | 0.2582501 | -0.0896497 | -0.0890791 | -0.1124490 | 0.0491570 | -0.2740025 | -0.1491519 | -0.0078816 | -0.1337635 | -0.2207150 | 0.1776808 | 1.0000000 | -0.0109690 | -0.0607542 | -0.0470701 | 0.0318901 | -0.0606497 | -0.0753713 | 0.1344147 | -0.0366332 | -0.0745566 | -0.0958183 |
| env.barren | -0.0977410 | -0.1129521 | -0.0766928 | -0.1373670 | -0.1574742 | -0.0459756 | -0.0051382 | -0.1537781 | -0.0483047 | -0.1263971 | -0.0345635 | 0.0167159 | -0.0021708 | 0.0151001 | -0.1002805 | -0.0630686 | 0.0078058 | -0.0844168 | -0.0580746 | 0.0973969 | -0.0109690 | 1.0000000 | -0.0116914 | -0.0361940 | -0.0154035 | -0.0116054 | -0.0235731 | -0.0790421 | -0.0543190 | -0.0802550 | 0.0973328 |
| env.water | 0.2893823 | 0.2673516 | 0.3146130 | 0.3077364 | 0.2459976 | 0.2895092 | -0.1606119 | 0.2693363 | 0.2836730 | -0.1456711 | 0.4128212 | -0.4386322 | 0.3366226 | -0.2590556 | 0.1226871 | 0.4173645 | -0.4383634 | 0.1904426 | 0.3417830 | -0.2664306 | -0.0607542 | -0.0116914 | 1.0000000 | -0.1898651 | -0.0920385 | 0.1375010 | -0.1324898 | -0.3658775 | 0.7287007 | 0.7473932 | 0.9149495 |
| env.savanna | -0.0024650 | 0.0269795 | -0.0212454 | -0.4152737 | -0.3587865 | -0.3454885 | 0.2264854 | -0.3170436 | -0.3479909 | -0.2570984 | -0.3398411 | 0.3434812 | -0.1551412 | 0.0693126 | 0.1672372 | -0.1202489 | 0.2711251 | -0.0413308 | 0.0150607 | 0.0973135 | -0.0470701 | -0.0361940 | -0.1898651 | 1.0000000 | -0.1167850 | -0.1579102 | -0.1249173 | -0.1437237 | -0.4725033 | 0.1212126 | -0.2018991 |
| env.woodysavanna | 0.0280840 | 0.0437261 | 0.0151050 | -0.1495779 | -0.0619659 | -0.1352915 | 0.1850820 | -0.0375880 | -0.1389403 | 0.0182359 | -0.0967487 | 0.0467406 | -0.0112436 | 0.0098816 | 0.0462821 | 0.0029894 | 0.0293186 | 0.0667723 | 0.0015716 | 0.0062860 | 0.0318901 | -0.0154035 | -0.0920385 | -0.1167850 | 1.0000000 | -0.0847233 | -0.1104016 | 0.0006886 | -0.0777231 | -0.1006558 | -0.1058182 |
| env.wetland | 0.1505896 | 0.2158389 | 0.0830030 | -0.1146293 | -0.1330635 | -0.0004524 | -0.0391527 | -0.1611847 | -0.0005642 | -0.0638922 | -0.0340330 | -0.0727980 | -0.0695311 | 0.0774311 | 0.1653903 | 0.1034344 | -0.0121391 | 0.1977084 | 0.0673108 | -0.1804121 | -0.0606497 | -0.0116054 | 0.1375010 | -0.1579102 | -0.0847233 | 1.0000000 | -0.1258117 | -0.3612828 | 0.2205404 | 0.2479464 | 0.2796946 |
| env.grass | -0.1029622 | -0.1911302 | -0.0941385 | -0.1512988 | 0.1157962 | -0.3242425 | 0.2617882 | 0.0346935 | -0.3307918 | 0.0956723 | -0.2794210 | 0.3469681 | -0.0158723 | -0.0168947 | -0.0906072 | -0.2159955 | 0.2074795 | -0.0870639 | -0.1438877 | 0.3347777 | -0.0753713 | -0.0235731 | -0.1324898 | -0.1249173 | -0.1104016 | -0.1258117 | 1.0000000 | -0.1337146 | -0.4646464 | 0.2073061 | -0.1003525 |
| env.npp | -0.5423448 | -0.5957826 | -0.4287304 | 0.2195306 | 0.0741961 | 0.2813411 | -0.2260104 | 0.0835534 | 0.2925463 | 0.2583383 | 0.1254955 | -0.1737652 | -0.1472418 | 0.1143688 | -0.6102193 | -0.3303117 | -0.0198950 | -0.5298813 | -0.4089540 | 0.3623028 | 0.1344147 | -0.0790421 | -0.3658775 | -0.1437237 | 0.0006886 | -0.3612828 | -0.1337146 | 1.0000000 | -0.0468240 | -0.5293037 | -0.4653541 |
| env.tree | 0.2241057 | 0.2394162 | 0.2510269 | 0.5598284 | 0.3196852 | 0.5902548 | -0.4273476 | 0.3395489 | 0.5897787 | -0.0288362 | 0.6191653 | -0.5951082 | 0.2873553 | -0.1926304 | 0.0503882 | 0.4202314 | -0.4936629 | 0.1649284 | 0.2748842 | -0.3968661 | -0.0366332 | -0.0543190 | 0.7287007 | -0.4725033 | -0.0777231 | 0.2205404 | -0.4646464 | -0.0468240 | 1.0000000 | 0.2718941 | 0.7398183 |
| env.nontree | 0.1944363 | 0.2026016 | 0.1763470 | -0.0677038 | 0.0073694 | -0.0208942 | 0.0504557 | -0.0039337 | -0.0290542 | -0.1901205 | 0.0444401 | -0.0668483 | 0.1459596 | -0.0852376 | 0.1630034 | 0.1793595 | -0.1095460 | 0.1344020 | 0.1941162 | -0.0874815 | -0.0745566 | -0.0802550 | 0.7473932 | 0.1212126 | -0.1006558 | 0.2479464 | 0.2073061 | -0.5293037 | 0.2718941 | 1.0000000 | 0.7880283 |
| env.nonveg | 0.3216861 | 0.3128536 | 0.3326884 | 0.2675771 | 0.2150625 | 0.2560326 | -0.1488980 | 0.2253595 | 0.2499643 | -0.1873301 | 0.3673542 | -0.3700583 | 0.3206495 | -0.2445389 | 0.1918470 | 0.4141109 | -0.3848206 | 0.2359080 | 0.3490410 | -0.3009313 | -0.0958183 | 0.0973328 | 0.9149495 | -0.2018991 | -0.1058182 | 0.2796946 | -0.1003525 | -0.4653541 | 0.7398183 | 0.7880283 | 1.0000000 |
Variable Importance analyses
Simple GLM
Code
presabs.glm <- PA %>%
dplyr::select(-c(1,2,3,5)) %>%
filter(!is.na(env.elev)&!is.na(env.bio_1))
glm.full<- glm(presabs ~.,
family = "binomial",
data = presabs.glm)
summary(glm.full)
Call:
glm(formula = presabs ~ ., family = "binomial", data = presabs.glm)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0457 -0.4784 -0.2840 0.2407 3.0053
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.449e+00 1.675e+01 -0.385 0.700271
env.bio_1 1.002e+01 7.765e+00 1.290 0.197053
env.bio_2 1.655e+00 7.393e+00 0.224 0.822903
env.bio_3 -3.063e+01 1.468e+01 -2.087 0.036920 *
env.bio_4 6.742e-01 1.992e+00 0.338 0.735063
env.bio_5 -1.125e+00 1.738e+00 -0.647 0.517424
env.bio_6 -1.200e+00 1.901e+00 -0.631 0.528034
env.bio_7 6.165e-01 6.734e-01 0.916 0.359888
env.bio_8 -3.007e-01 2.204e+00 -0.136 0.891470
env.bio_9 1.903e+00 2.260e+00 0.842 0.399948
env.bio_10 1.153e+00 4.598e-01 2.508 0.012136 *
env.bio_11 -7.415e-01 5.449e-01 -1.361 0.173618
env.bio_12 -2.929e+00 2.293e+00 -1.277 0.201483
env.bio_13 2.773e+00 1.597e+00 1.736 0.082553 .
env.bio_14 -1.285e+01 7.998e+00 -1.607 0.108153
env.bio_15 6.405e+05 1.262e+06 0.508 0.611708
env.bio_16 -1.074e+06 2.115e+06 -0.508 0.611711
env.bio_17 -9.123e+05 1.797e+06 -0.508 0.611712
env.bio_18 2.068e+00 9.438e-01 2.191 0.028433 *
env.bio_19 3.289e+00 1.170e+00 2.812 0.004929 **
env.elev 2.633e-01 9.947e-01 0.265 0.791259
env.urban 6.511e-01 2.140e-01 3.043 0.002344 **
env.barren -2.460e+01 8.456e+01 -0.291 0.771101
env.water 6.383e-02 3.088e-01 0.207 0.836207
env.savanna 3.281e-01 3.135e-01 1.046 0.295457
env.woodysavanna 4.248e-01 2.672e-01 1.590 0.111873
env.wetland 5.931e-02 1.900e-01 0.312 0.754930
env.grass -1.979e-01 4.952e-01 -0.400 0.689413
env.npp 1.519e+00 3.907e-01 3.889 0.000101 ***
env.tree 2.681e-01 6.558e-01 0.409 0.682594
env.nontree -3.012e-01 5.858e-01 -0.514 0.607172
env.nonveg 6.917e-02 7.274e-01 0.095 0.924246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 646.18 on 558 degrees of freedom
Residual deviance: 395.05 on 527 degrees of freedom
AIC: 459.05
Number of Fisher Scoring iterations: 12Code
step(glm.full) # step might not work with gam so glmStart: AIC=459.05
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 +
env.bio_6 + env.bio_7 + env.bio_8 + env.bio_9 + env.bio_10 +
env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 +
env.bio_16 + env.bio_17 + env.bio_18 + env.bio_19 + env.elev +
env.urban + env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.grass + env.npp + env.tree + env.nontree +
env.nonveg
Df Deviance AIC
- env.nonveg 1 395.06 457.06
- env.bio_8 1 395.07 457.07
- env.water 1 395.09 457.09
- env.bio_2 1 395.10 457.10
- env.elev 1 395.12 457.12
- env.wetland 1 395.14 457.14
- env.bio_4 1 395.16 457.16
- env.grass 1 395.21 457.21
- env.tree 1 395.21 457.21
- env.bio_17 1 395.31 457.31
- env.bio_16 1 395.31 457.31
- env.bio_15 1 395.31 457.31
- env.nontree 1 395.31 457.31
- env.barren 1 395.38 457.38
- env.bio_6 1 395.44 457.44
- env.bio_5 1 395.47 457.47
- env.bio_9 1 395.75 457.75
- env.bio_7 1 395.90 457.90
- env.savanna 1 396.17 458.17
- env.bio_12 1 396.69 458.69
- env.bio_1 1 396.73 458.73
- env.bio_11 1 397.02 459.02
<none> 395.05 459.05
- env.woodysavanna 1 397.54 459.54
- env.bio_14 1 397.66 459.66
- env.bio_13 1 398.06 460.06
- env.bio_3 1 399.47 461.47
- env.bio_18 1 400.30 462.30
- env.bio_10 1 401.51 463.51
- env.bio_19 1 403.72 465.72
- env.urban 1 404.62 466.62
- env.npp 1 413.46 475.46
Step: AIC=457.06
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 +
env.bio_6 + env.bio_7 + env.bio_8 + env.bio_9 + env.bio_10 +
env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 +
env.bio_16 + env.bio_17 + env.bio_18 + env.bio_19 + env.elev +
env.urban + env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_8 1 395.07 455.07
- env.water 1 395.10 455.10
- env.bio_2 1 395.11 455.11
- env.elev 1 395.12 455.12
- env.wetland 1 395.15 455.15
- env.bio_4 1 395.17 455.17
- env.grass 1 395.21 455.21
- env.bio_17 1 395.32 455.32
- env.bio_16 1 395.32 455.32
- env.bio_15 1 395.32 455.32
- env.barren 1 395.39 455.39
- env.bio_6 1 395.47 455.47
- env.bio_5 1 395.49 455.49
- env.tree 1 395.53 455.53
- env.nontree 1 395.55 455.55
- env.bio_9 1 395.79 455.79
- env.bio_7 1 395.92 455.92
- env.savanna 1 396.22 456.22
- env.bio_1 1 396.83 456.83
- env.bio_12 1 396.97 456.97
- env.bio_11 1 397.02 457.02
<none> 395.06 457.06
- env.woodysavanna 1 397.61 457.61
- env.bio_14 1 397.87 457.87
- env.bio_13 1 398.27 458.27
- env.bio_3 1 399.65 459.65
- env.bio_18 1 400.30 460.30
- env.bio_10 1 401.53 461.53
- env.bio_19 1 403.80 463.80
- env.urban 1 404.66 464.66
- env.npp 1 415.42 475.42
Step: AIC=455.07
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 +
env.bio_6 + env.bio_7 + env.bio_9 + env.bio_10 + env.bio_11 +
env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 + env.bio_16 +
env.bio_17 + env.bio_18 + env.bio_19 + env.elev + env.urban +
env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.water 1 395.12 453.12
- env.bio_2 1 395.12 453.12
- env.elev 1 395.12 453.12
- env.bio_4 1 395.18 453.18
- env.wetland 1 395.18 453.18
- env.grass 1 395.21 453.21
- env.bio_17 1 395.35 453.35
- env.bio_16 1 395.35 453.35
- env.bio_15 1 395.35 453.35
- env.barren 1 395.41 453.41
- env.bio_6 1 395.50 453.50
- env.tree 1 395.60 453.60
- env.nontree 1 395.65 453.65
- env.bio_9 1 395.88 453.88
- env.bio_7 1 395.92 453.92
- env.savanna 1 396.29 454.29
- env.bio_5 1 396.51 454.51
- env.bio_1 1 396.84 454.84
- env.bio_12 1 396.97 454.97
<none> 395.07 455.07
- env.bio_11 1 397.09 455.09
- env.woodysavanna 1 397.61 455.61
- env.bio_14 1 397.87 455.87
- env.bio_13 1 398.34 456.34
- env.bio_3 1 399.65 457.65
- env.bio_18 1 400.31 458.31
- env.bio_10 1 401.53 459.53
- env.bio_19 1 403.82 461.82
- env.urban 1 404.71 462.71
- env.npp 1 415.96 473.96
Step: AIC=453.12
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 +
env.bio_6 + env.bio_7 + env.bio_9 + env.bio_10 + env.bio_11 +
env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 + env.bio_16 +
env.bio_17 + env.bio_18 + env.bio_19 + env.elev + env.urban +
env.barren + env.savanna + env.woodysavanna + env.wetland +
env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_2 1 395.17 451.17
- env.elev 1 395.18 451.18
- env.wetland 1 395.19 451.19
- env.bio_4 1 395.23 451.23
- env.grass 1 395.26 451.26
- env.bio_17 1 395.38 451.38
- env.bio_16 1 395.38 451.38
- env.bio_15 1 395.38 451.38
- env.barren 1 395.51 451.51
- env.bio_6 1 395.58 451.58
- env.nontree 1 395.81 451.81
- env.bio_9 1 395.96 451.96
- env.bio_7 1 395.98 451.98
- env.tree 1 396.11 452.11
- env.savanna 1 396.31 452.31
- env.bio_5 1 396.61 452.61
- env.bio_1 1 396.91 452.91
- env.bio_12 1 397.00 453.00
- env.bio_11 1 397.10 453.10
<none> 395.12 453.12
- env.woodysavanna 1 397.64 453.64
- env.bio_14 1 397.97 453.97
- env.bio_13 1 398.34 454.34
- env.bio_3 1 399.78 455.78
- env.bio_18 1 400.35 456.35
- env.bio_10 1 401.69 457.69
- env.bio_19 1 403.83 459.83
- env.urban 1 404.72 460.72
- env.npp 1 416.84 472.84
Step: AIC=451.17
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 +
env.bio_18 + env.bio_19 + env.elev + env.urban + env.barren +
env.savanna + env.woodysavanna + env.wetland + env.grass +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.elev 1 395.21 449.21
- env.wetland 1 395.23 449.23
- env.bio_4 1 395.25 449.25
- env.grass 1 395.30 449.30
- env.bio_17 1 395.45 449.45
- env.bio_16 1 395.45 449.45
- env.bio_15 1 395.45 449.45
- env.barren 1 395.56 449.56
- env.bio_6 1 395.63 449.63
- env.nontree 1 395.87 449.87
- env.bio_9 1 396.03 450.03
- env.bio_7 1 396.08 450.08
- env.tree 1 396.31 450.31
- env.savanna 1 396.40 450.40
- env.bio_5 1 396.64 450.64
- env.bio_11 1 397.10 451.10
- env.bio_1 1 397.14 451.14
<none> 395.17 451.17
- env.bio_12 1 397.18 451.18
- env.woodysavanna 1 397.78 451.78
- env.bio_13 1 398.51 452.51
- env.bio_14 1 400.08 454.08
- env.bio_18 1 400.40 454.40
- env.bio_3 1 400.92 454.92
- env.bio_10 1 402.24 456.24
- env.bio_19 1 403.85 457.85
- env.urban 1 406.32 460.32
- env.npp 1 419.52 473.52
Step: AIC=449.21
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 +
env.bio_18 + env.bio_19 + env.urban + env.barren + env.savanna +
env.woodysavanna + env.wetland + env.grass + env.npp + env.tree +
env.nontree
Df Deviance AIC
- env.wetland 1 395.28 447.28
- env.bio_4 1 395.31 447.31
- env.grass 1 395.35 447.35
- env.bio_17 1 395.51 447.51
- env.bio_16 1 395.51 447.51
- env.bio_15 1 395.51 447.51
- env.barren 1 395.62 447.62
- env.bio_6 1 395.65 447.65
- env.nontree 1 395.87 447.87
- env.bio_9 1 396.06 448.06
- env.tree 1 396.36 448.36
- env.bio_7 1 396.40 448.40
- env.savanna 1 396.49 448.49
- env.bio_5 1 396.78 448.78
- env.bio_11 1 397.10 449.10
<none> 395.21 449.21
- env.bio_12 1 397.28 449.28
- env.bio_1 1 397.38 449.38
- env.woodysavanna 1 397.84 449.84
- env.bio_13 1 398.62 450.62
- env.bio_18 1 400.60 452.60
- env.bio_14 1 400.71 452.71
- env.bio_3 1 401.24 453.24
- env.bio_10 1 402.25 454.25
- env.bio_19 1 403.93 455.93
- env.urban 1 406.50 458.50
- env.npp 1 420.09 472.09
Step: AIC=447.28
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 +
env.bio_18 + env.bio_19 + env.urban + env.barren + env.savanna +
env.woodysavanna + env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_4 1 395.37 445.37
- env.grass 1 395.45 445.45
- env.bio_17 1 395.59 445.59
- env.bio_16 1 395.59 445.59
- env.bio_15 1 395.59 445.59
- env.barren 1 395.70 445.70
- env.bio_6 1 395.75 445.75
- env.nontree 1 395.91 445.91
- env.bio_9 1 396.18 446.18
- env.tree 1 396.46 446.46
- env.bio_7 1 396.49 446.49
- env.savanna 1 396.50 446.50
- env.bio_5 1 396.84 446.84
- env.bio_11 1 397.16 447.16
<none> 395.28 447.28
- env.bio_12 1 397.31 447.31
- env.bio_1 1 397.50 447.50
- env.woodysavanna 1 397.85 447.85
- env.bio_13 1 398.62 448.62
- env.bio_18 1 400.66 450.66
- env.bio_14 1 400.91 450.91
- env.bio_3 1 401.46 451.46
- env.bio_10 1 402.35 452.35
- env.bio_19 1 403.93 453.93
- env.urban 1 406.51 456.51
- env.npp 1 421.56 471.56
Step: AIC=445.37
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 + env.bio_18 +
env.bio_19 + env.urban + env.barren + env.savanna + env.woodysavanna +
env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.grass 1 395.56 443.56
- env.bio_17 1 395.75 443.75
- env.bio_16 1 395.75 443.75
- env.bio_15 1 395.75 443.75
- env.barren 1 395.77 443.77
- env.bio_6 1 395.93 443.93
- env.nontree 1 396.04 444.04
- env.tree 1 396.50 444.50
- env.bio_9 1 396.55 444.55
- env.savanna 1 396.56 444.56
- env.bio_7 1 396.63 444.63
- env.bio_11 1 397.20 445.20
<none> 395.37 445.37
- env.bio_1 1 397.59 445.59
- env.woodysavanna 1 397.87 445.87
- env.bio_12 1 398.30 446.30
- env.bio_5 1 398.42 446.42
- env.bio_13 1 399.18 447.18
- env.bio_18 1 400.80 448.80
- env.bio_3 1 401.64 449.64
- env.bio_14 1 401.99 449.99
- env.bio_19 1 404.31 452.31
- env.bio_10 1 404.85 452.85
- env.urban 1 406.53 454.53
- env.npp 1 421.59 469.59
Step: AIC=443.56
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 + env.bio_18 +
env.bio_19 + env.urban + env.barren + env.savanna + env.woodysavanna +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.barren 1 395.97 441.97
- env.bio_17 1 395.98 441.98
- env.bio_16 1 395.98 441.98
- env.bio_15 1 395.98 441.98
- env.bio_6 1 396.15 442.15
- env.bio_9 1 396.80 442.80
- env.bio_7 1 397.01 443.01
- env.nontree 1 397.19 443.19
- env.bio_11 1 397.42 443.42
<none> 395.56 443.56
- env.bio_1 1 397.66 443.66
- env.bio_12 1 398.39 444.39
- env.bio_5 1 398.79 444.79
- env.savanna 1 398.99 444.99
- env.woodysavanna 1 399.12 445.12
- env.tree 1 399.36 445.36
- env.bio_13 1 399.38 445.38
- env.bio_18 1 400.98 446.98
- env.bio_3 1 401.69 447.69
- env.bio_14 1 402.00 448.00
- env.bio_19 1 404.91 450.91
- env.bio_10 1 405.19 451.19
- env.urban 1 408.76 454.76
- env.npp 1 422.51 468.51
Step: AIC=441.97
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_16 + env.bio_17 + env.bio_18 +
env.bio_19 + env.urban + env.savanna + env.woodysavanna +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_17 1 396.46 440.46
- env.bio_16 1 396.46 440.46
- env.bio_15 1 396.46 440.46
- env.bio_6 1 396.54 440.54
- env.bio_9 1 397.20 441.20
- env.bio_7 1 397.46 441.46
- env.nontree 1 397.54 441.54
- env.bio_11 1 397.77 441.77
<none> 395.97 441.97
- env.bio_1 1 398.08 442.08
- env.bio_12 1 398.87 442.87
- env.bio_5 1 399.30 443.30
- env.savanna 1 399.46 443.46
- env.woodysavanna 1 399.53 443.53
- env.tree 1 399.68 443.68
- env.bio_13 1 399.80 443.80
- env.bio_18 1 401.50 445.50
- env.bio_3 1 401.97 445.97
- env.bio_14 1 402.28 446.28
- env.bio_19 1 405.38 449.38
- env.bio_10 1 405.46 449.46
- env.urban 1 409.63 453.63
- env.npp 1 425.83 469.83
Step: AIC=440.46
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_16 + env.bio_18 + env.bio_19 +
env.urban + env.savanna + env.woodysavanna + env.npp + env.tree +
env.nontree
Df Deviance AIC
- env.bio_16 1 396.71 438.71
- env.bio_6 1 397.11 439.11
- env.bio_9 1 397.84 439.84
- env.bio_7 1 398.04 440.04
- env.nontree 1 398.08 440.08
- env.bio_11 1 398.32 440.32
<none> 396.46 440.46
- env.bio_1 1 398.63 440.63
- env.bio_12 1 399.19 441.19
- env.bio_5 1 399.87 441.87
- env.bio_13 1 400.02 442.02
- env.savanna 1 400.06 442.06
- env.tree 1 400.12 442.12
- env.woodysavanna 1 400.27 442.27
- env.bio_18 1 402.04 444.04
- env.bio_3 1 402.69 444.69
- env.bio_14 1 403.02 445.02
- env.bio_15 1 404.05 446.05
- env.bio_19 1 405.81 447.81
- env.bio_10 1 406.17 448.17
- env.urban 1 409.72 451.72
- env.npp 1 426.16 468.16
Step: AIC=438.71
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_9 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_18 + env.bio_19 + env.urban +
env.savanna + env.woodysavanna + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_6 1 397.41 437.41
- env.bio_9 1 398.17 438.17
- env.bio_7 1 398.35 438.35
- env.nontree 1 398.45 438.45
<none> 396.71 438.71
- env.bio_11 1 398.74 438.74
- env.bio_1 1 398.88 438.88
- env.bio_5 1 399.90 439.90
- env.savanna 1 400.25 440.25
- env.tree 1 400.27 440.27
- env.woodysavanna 1 400.30 440.30
- env.bio_13 1 401.24 441.24
- env.bio_12 1 401.63 441.63
- env.bio_18 1 402.11 442.11
- env.bio_14 1 403.26 443.26
- env.bio_3 1 403.78 443.78
- env.bio_15 1 404.74 444.74
- env.bio_19 1 405.91 445.91
- env.bio_10 1 406.42 446.42
- env.urban 1 409.73 449.73
- env.npp 1 426.78 466.78
Step: AIC=437.41
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_7 + env.bio_9 +
env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_15 + env.bio_18 + env.bio_19 + env.urban + env.savanna +
env.woodysavanna + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_7 1 398.74 436.74
- env.bio_11 1 399.05 437.05
- env.nontree 1 399.19 437.19
- env.bio_9 1 399.30 437.30
<none> 397.41 437.41
- env.bio_1 1 399.42 437.42
- env.bio_5 1 400.82 438.82
- env.savanna 1 400.96 438.96
- env.woodysavanna 1 400.98 438.98
- env.tree 1 401.27 439.27
- env.bio_13 1 401.67 439.67
- env.bio_12 1 401.82 439.82
- env.bio_18 1 402.29 440.29
- env.bio_14 1 403.56 441.56
- env.bio_3 1 404.01 442.01
- env.bio_15 1 405.04 443.04
- env.bio_19 1 406.29 444.29
- env.bio_10 1 407.91 445.91
- env.urban 1 410.67 448.67
- env.npp 1 429.75 467.75
Step: AIC=436.74
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_9 + env.bio_10 +
env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 +
env.bio_18 + env.bio_19 + env.urban + env.savanna + env.woodysavanna +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_9 1 399.30 435.30
- env.bio_1 1 400.05 436.05
- env.nontree 1 400.14 436.14
<none> 398.74 436.74
- env.bio_11 1 400.99 436.99
- env.bio_5 1 401.30 437.30
- env.savanna 1 402.11 438.11
- env.tree 1 402.93 438.93
- env.bio_12 1 402.95 438.95
- env.woodysavanna 1 403.20 439.20
- env.bio_14 1 403.87 439.87
- env.bio_18 1 404.38 440.38
- env.bio_3 1 404.47 440.47
- env.bio_15 1 406.32 442.32
- env.bio_13 1 406.86 442.86
- env.bio_19 1 409.51 445.51
- env.bio_10 1 409.67 445.67
- env.urban 1 411.94 447.94
- env.npp 1 436.25 472.25
Step: AIC=435.3
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_10 + env.bio_11 +
env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 + env.bio_18 +
env.bio_19 + env.urban + env.savanna + env.woodysavanna +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_1 1 400.20 434.20
- env.nontree 1 400.60 434.60
- env.bio_11 1 401.02 435.02
<none> 399.30 435.30
- env.savanna 1 402.51 436.51
- env.bio_5 1 402.83 436.83
- env.woodysavanna 1 403.38 437.38
- env.bio_12 1 403.55 437.55
- env.bio_14 1 403.90 437.90
- env.bio_3 1 404.49 438.49
- env.tree 1 404.58 438.58
- env.bio_18 1 404.70 438.70
- env.bio_15 1 407.21 441.21
- env.bio_13 1 407.91 441.91
- env.bio_19 1 410.35 444.35
- env.urban 1 411.96 445.96
- env.bio_10 1 419.09 453.09
- env.npp 1 438.13 472.13
Step: AIC=434.2
presabs ~ env.bio_3 + env.bio_5 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + env.bio_15 + env.bio_18 + env.bio_19 +
env.urban + env.savanna + env.woodysavanna + env.npp + env.tree +
env.nontree
Df Deviance AIC
- env.nontree 1 401.87 433.87
- env.bio_11 1 401.99 433.99
<none> 400.20 434.20
- env.savanna 1 402.77 434.77
- env.bio_5 1 403.07 435.07
- env.bio_12 1 403.55 435.55
- env.woodysavanna 1 403.91 435.91
- env.tree 1 404.66 436.66
- env.bio_18 1 406.41 438.41
- env.bio_15 1 407.57 439.57
- env.bio_13 1 407.93 439.93
- env.bio_14 1 408.05 440.05
- env.urban 1 411.98 443.98
- env.bio_19 1 413.17 445.17
- env.bio_3 1 414.85 446.85
- env.bio_10 1 419.61 451.61
- env.npp 1 439.78 471.78
Step: AIC=433.87
presabs ~ env.bio_3 + env.bio_5 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + env.bio_15 + env.bio_18 + env.bio_19 +
env.urban + env.savanna + env.woodysavanna + env.npp + env.tree
Df Deviance AIC
- env.bio_11 1 403.02 433.02
- env.savanna 1 403.36 433.36
<none> 401.87 433.87
- env.bio_12 1 405.08 435.08
- env.woodysavanna 1 405.20 435.20
- env.tree 1 405.29 435.29
- env.bio_5 1 405.31 435.31
- env.bio_13 1 408.57 438.57
- env.bio_18 1 408.58 438.58
- env.bio_15 1 409.85 439.85
- env.bio_14 1 411.18 441.18
- env.urban 1 413.50 443.50
- env.bio_19 1 414.79 444.79
- env.bio_3 1 416.66 446.66
- env.bio_10 1 425.80 455.80
- env.npp 1 475.17 505.17
Step: AIC=433.02
presabs ~ env.bio_3 + env.bio_5 + env.bio_10 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_18 + env.bio_19 + env.urban +
env.savanna + env.woodysavanna + env.npp + env.tree
Df Deviance AIC
- env.savanna 1 404.75 432.75
<none> 403.02 433.02
- env.bio_12 1 405.09 433.09
- env.tree 1 406.53 434.53
- env.woodysavanna 1 407.19 435.19
- env.bio_13 1 409.24 437.24
- env.bio_5 1 409.79 437.79
- env.bio_15 1 409.86 437.86
- env.bio_18 1 410.20 438.20
- env.bio_14 1 412.19 440.19
- env.urban 1 415.31 443.31
- env.bio_19 1 416.23 444.23
- env.bio_3 1 416.71 444.71
- env.bio_10 1 432.36 460.36
- env.npp 1 475.17 503.17
Step: AIC=432.75
presabs ~ env.bio_3 + env.bio_5 + env.bio_10 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_15 + env.bio_18 + env.bio_19 + env.urban +
env.woodysavanna + env.npp + env.tree
Df Deviance AIC
<none> 404.75 432.75
- env.tree 1 406.88 432.88
- env.bio_12 1 407.14 433.14
- env.woodysavanna 1 407.83 433.83
- env.bio_13 1 411.02 437.02
- env.bio_18 1 411.89 437.89
- env.bio_15 1 412.36 438.36
- env.bio_5 1 413.70 439.70
- env.bio_14 1 415.71 441.71
- env.urban 1 416.52 442.52
- env.bio_19 1 418.72 444.72
- env.bio_3 1 419.95 445.95
- env.bio_10 1 433.42 459.42
- env.npp 1 475.32 501.32
Call: glm(formula = presabs ~ env.bio_3 + env.bio_5 + env.bio_10 +
env.bio_12 + env.bio_13 + env.bio_14 + env.bio_15 + env.bio_18 +
env.bio_19 + env.urban + env.woodysavanna + env.npp + env.tree,
family = "binomial", data = presabs.glm)
Coefficients:
(Intercept) env.bio_3 env.bio_5 env.bio_10
-1.8505 -14.7065 -1.3637 1.6043
env.bio_12 env.bio_13 env.bio_14 env.bio_15
-1.2054 1.7429 -6.7205 5.9447
env.bio_18 env.bio_19 env.urban env.woodysavanna
2.2964 3.7907 0.5882 0.3972
env.npp env.tree
1.6473 0.2855
Degrees of Freedom: 558 Total (i.e. Null); 545 Residual
Null Deviance: 646.2
Residual Deviance: 404.8 AIC: 432.8Boosted regression trees
Boosted regression trees were fitted using packages: gbm and dismo — specifically the gbm.step() function (Hijmans et al., 2017)
Code
# cross-validation optimisation of a boosted regression tree model
brt <- gbm.step(data = PA,
gbm.x = 6:ncol(PA),
gbm.y = "presabs",
family = "bernoulli")
GBM STEP - version 2.9
Performing cross-validation optimisation of a boosted regression tree model
for NA and using a family of bernoulli
Using 565 observations and 31 predictors
creating 10 initial models of 50 trees
folds are stratified by prevalence
total mean deviance = 1.1502
tolerance is fixed at 0.0012
ntrees resid. dev.
50 0.9974
now adding trees...
100 0.9411
150 0.9056
200 0.8745
250 0.8505
300 0.8301
350 0.8163
400 0.8005
450 0.7882
500 0.777
550 0.7667
600 0.759
650 0.7538
700 0.7488
750 0.7421
800 0.7365
850 0.7303
900 0.7261
950 0.7229
1000 0.7193
1050 0.7157
1100 0.7124
1150 0.7102
1200 0.7069
1250 0.7043
1300 0.7009
1350 0.6979
1400 0.6953
1450 0.6944
1500 0.6916
1550 0.6892
1600 0.6877
1650 0.6855
1700 0.6829
1750 0.6805
1800 0.6785
1850 0.6776
1900 0.6754
1950 0.6744
2000 0.6726
2050 0.6717
2100 0.6698
2150 0.6688
2200 0.6678
2250 0.6665
2300 0.6658
2350 0.6646
2400 0.6641
2450 0.6635
2500 0.6632
2550 0.6629
2600 0.6629
2650 0.6626
2700 0.6616
2750 0.6614
2800 0.6602
2850 0.6597
2900 0.6595
2950 0.6595
3000 0.6591
3050 0.6585
3100 0.659
3150 0.6596
3200 0.6588
3250 0.6586
3300 0.659
3350 0.658
3400 0.6573
3450 0.6569
3500 0.6569
3550 0.6563
3600 0.6561
3650 0.6555
3700 0.6561
3750 0.6559
3800 0.6562
3850 0.6559
3900 0.6567
3950 0.6576
4000 0.6566
4050 0.6566
4100 0.6564
4150 0.6567 
mean total deviance = 1.15
mean residual deviance = 0.387
estimated cv deviance = 0.655 ; se = 0.073
training data correlation = 0.863
cv correlation = 0.709 ; se = 0.04
training data AUC score = 0.974
cv AUC score = 0.906 ; se = 0.022
elapsed time - 0.21 minutes Code
summary(brt)
var rel.inf
env.npp env.npp 29.2632535
env.bio_7 env.bio_7 10.7616260
env.nontree env.nontree 8.4807878
env.woodysavanna env.woodysavanna 6.9180109
env.bio_10 env.bio_10 5.5806587
env.urban env.urban 4.5147804
env.bio_9 env.bio_9 4.3934988
env.nonveg env.nonveg 3.5207357
env.bio_19 env.bio_19 2.9252155
env.bio_4 env.bio_4 2.6741733
env.bio_18 env.bio_18 2.2293873
env.bio_12 env.bio_12 2.0622015
env.bio_13 env.bio_13 2.0095698
env.bio_8 env.bio_8 1.8953799
env.bio_3 env.bio_3 1.8859477
env.tree env.tree 1.6106334
env.bio_14 env.bio_14 1.4619804
env.bio_11 env.bio_11 1.4407766
env.bio_17 env.bio_17 1.2190097
env.bio_16 env.bio_16 1.1231869
env.elev env.elev 0.9689513
env.bio_1 env.bio_1 0.6177356
env.bio_2 env.bio_2 0.5372641
env.bio_15 env.bio_15 0.4935636
env.bio_6 env.bio_6 0.3935883
env.wetland env.wetland 0.2853478
env.bio_5 env.bio_5 0.2526800
env.grass env.grass 0.2462057
env.savanna env.savanna 0.2338498
env.barren env.barren 0.0000000
env.water env.water 0.0000000Code
variables_brt <- brt$contributions[1:6,] %>% pull(var)
#exploration of shape of relationships
#gbm.plot(brt, n.plots = 12, plot.layout=c(6, 2))Random forest
Code
presabs.rf <- PA %>%
dplyr::select(-c(1,2,3,5)) %>%
mutate(presabs = as.factor(presabs))
rf <- randomForest(presabs ~ .,
data=presabs.rf,
importance=T,
nperm=2, # two permutations per tree to estimate importance
na.action=na.omit,
mtry= 1/3*ncol(presabs.rf)-1)
varImpPlot(rf, type=2)
Code
variables_rf <- rf$importance %>% as_tibble(rownames = 'var') %>% arrange(desc(MeanDecreaseGini)) %>% head(n=6) %>% pull(var)Ranger
Code
presabs.ranger <- PA %>%
dplyr::select(-c(1,2,3,5)) %>%
filter(!if_any(everything(), is.na)) %>%
mutate(presabs = as.factor(presabs))
## Learn the model:
ranger <- ranger(presabs ~ .,
data = presabs.ranger,
num.trees = 150,
mtry = 1/3*ncol(presabs.ranger)-1,
min.node.size = 5,
max.depth = NULL,
write.forest = TRUE,
importance = "impurity")
# Get the variable importance
importance(ranger) env.bio_1 env.bio_2 env.bio_3 env.bio_4
4.1698726 4.4656697 3.8640164 6.2105000
env.bio_5 env.bio_6 env.bio_7 env.bio_8
5.1095798 4.8119835 9.7190390 5.8843484
env.bio_9 env.bio_10 env.bio_11 env.bio_12
8.0349541 12.8025151 4.3685936 6.9179461
env.bio_13 env.bio_14 env.bio_15 env.bio_16
6.1818748 6.3221521 3.9493733 6.2083303
env.bio_17 env.bio_18 env.bio_19 env.elev
5.9348606 4.3298542 4.4513588 5.1778142
env.urban env.barren env.water env.savanna
4.8001362 0.4453489 0.5211972 3.1991353
env.woodysavanna env.wetland env.grass env.npp
6.4762220 1.0071619 2.4821251 37.0187370
env.tree env.nontree env.nonveg
7.8036443 14.3119810 10.6692806 Code
importance(ranger) %>%
as.data.frame(row.names = names(.)) %>%
setNames(c("Importance")) %>%
rownames_to_column("covariate") %>%
# mutate(covariate = case_when(
# covariate == "env.bio_1" ~ "Annual Mean Temperature",
# )) %>%
ggplot(aes(x = Importance, y = reorder(covariate, Importance))) +
ylab('')+
geom_point() + theme_bw()
Code
variables_ranger <- importance(ranger) %>% as_tibble(rownames = 'var') %>% arrange(desc(value)) %>% head(n=6) %>% pull(var)Correlation
Correlation between the six more important variables detected.
- Boosted regression tree: env.npp, env.bio_7, env.nontree, env.woodysavanna, env.bio_10, env.urban
- Random forest: env.npp, env.nontree, env.bio_10, env.nonveg, env.bio_7, env.bio_12
- Ranger: env.npp, env.nontree, env.bio_10, env.nonveg, env.bio_7, env.bio_9
Code
selectedVariables <- unique(c(variables_brt, variables_rf, variables_ranger))
PA %>%
filter(!if_any(everything(), is.na)) %>%
dplyr::select(selectedVariables) %>%
pairs()
Code
PA %>%
filter(!if_any(everything(), is.na)) %>%
dplyr::select(selectedVariables) %>%
cor() %>% kable()| env.npp | env.bio_7 | env.nontree | env.woodysavanna | env.bio_10 | env.urban | env.nonveg | env.bio_12 | env.bio_9 | |
|---|---|---|---|---|---|---|---|---|---|
| env.npp | 1.0000000 | -0.2260104 | -0.5293037 | 0.0006886 | 0.2583383 | 0.1344147 | -0.4653541 | -0.1737652 | 0.2925463 |
| env.bio_7 | -0.2260104 | 1.0000000 | 0.0504557 | 0.1850820 | -0.1345391 | 0.0225795 | -0.1488980 | 0.4185941 | -0.8782263 |
| env.nontree | -0.5293037 | 0.0504557 | 1.0000000 | -0.1006558 | -0.1901205 | -0.0745566 | 0.7880283 | -0.0668483 | -0.0290542 |
| env.woodysavanna | 0.0006886 | 0.1850820 | -0.1006558 | 1.0000000 | 0.0182359 | 0.0318901 | -0.1058182 | 0.0467406 | -0.1389403 |
| env.bio_10 | 0.2583383 | -0.1345391 | -0.1901205 | 0.0182359 | 1.0000000 | 0.2582501 | -0.1873301 | -0.1343382 | 0.1840244 |
| env.urban | 0.1344147 | 0.0225795 | -0.0745566 | 0.0318901 | 0.2582501 | 1.0000000 | -0.0958183 | -0.0890791 | -0.0294760 |
| env.nonveg | -0.4653541 | -0.1488980 | 0.7880283 | -0.1058182 | -0.1873301 | -0.0958183 | 1.0000000 | -0.3700583 | 0.2499643 |
| env.bio_12 | -0.1737652 | 0.4185941 | -0.0668483 | 0.0467406 | -0.1343382 | -0.0890791 | -0.3700583 | 1.0000000 | -0.5272262 |
| env.bio_9 | 0.2925463 | -0.8782263 | -0.0290542 | -0.1389403 | 0.1840244 | -0.0294760 | 0.2499643 | -0.5272262 | 1.0000000 |
Code
tmpFiles(remove=T)