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 pardalis Variable Selection
Variable Selection for Leopardus pardalis, 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()Species’ preferences
We will use the data from both periods
Code
PA_time1 <- readRDS('data/species_POPA_data/PA_lpardalis_time1_blobs.rds')%>% ungroup()
PA_time2 <- readRDS('data/species_POPA_data/PA_lpardalis_time2_blobs.rds')%>% ungroup()
PA_time1 %>% st_drop_geometry() %>% head() %>% kable()| ID | presence | temporalSpan | effort | blobArea |
|---|---|---|---|---|
| 1 | 1 | 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 | 1 | 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
-2.9049 -0.7945 0.0678 0.8795 2.1595
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -8.653e+00 1.529e+01 -0.566 0.57131
env.bio_1 2.380e+01 7.537e+00 3.158 0.00159 **
env.bio_2 4.054e+00 7.779e+00 0.521 0.60224
env.bio_3 -2.926e+01 1.417e+01 -2.066 0.03884 *
env.bio_4 1.608e+00 2.228e+00 0.722 0.47039
env.bio_5 -3.347e+00 1.543e+00 -2.169 0.03010 *
env.bio_6 -3.248e+00 1.624e+00 -2.000 0.04546 *
env.bio_7 1.924e+00 7.820e-01 2.460 0.01388 *
env.bio_8 2.035e+00 2.179e+00 0.934 0.35033
env.bio_9 5.525e+00 2.085e+00 2.649 0.00806 **
env.bio_10 -5.355e-01 4.266e-01 -1.255 0.20936
env.bio_11 -8.949e-01 5.709e-01 -1.568 0.11697
env.bio_12 2.365e+00 1.837e+00 1.287 0.19796
env.bio_13 -9.988e-01 1.373e+00 -0.728 0.46685
env.bio_14 -9.770e+00 7.797e+00 -1.253 0.21019
env.bio_15 5.392e+05 9.051e+05 0.596 0.55137
env.bio_16 -9.038e+05 1.517e+06 -0.596 0.55137
env.bio_17 -7.680e+05 1.289e+06 -0.596 0.55137
env.bio_18 3.330e-01 9.266e-01 0.359 0.71927
env.bio_19 -1.331e+00 1.480e+00 -0.900 0.36819
env.elev 6.542e-01 1.049e+00 0.624 0.53271
env.urban 7.417e-01 2.616e-01 2.836 0.00458 **
env.barren -3.579e+01 7.713e+01 -0.464 0.64263
env.water -3.894e-01 1.958e-01 -1.989 0.04671 *
env.savanna 4.282e-01 2.127e-01 2.014 0.04405 *
env.woodysavanna 2.042e-01 2.176e-01 0.938 0.34813
env.wetland 9.501e-02 8.376e-02 1.134 0.25662
env.grass 6.547e-02 3.048e-01 0.215 0.82993
env.npp 5.985e-01 3.156e-01 1.897 0.05787 .
env.tree 1.290e+00 4.805e-01 2.685 0.00725 **
env.nontree -1.174e-01 4.293e-01 -0.273 0.78450
env.nonveg -2.982e-01 5.562e-01 -0.536 0.59187
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 774.89 on 558 degrees of freedom
Residual deviance: 557.73 on 527 degrees of freedom
AIC: 621.73
Number of Fisher Scoring iterations: 11Code
step(glm.full) # step might not work with gam so glmStart: AIC=621.73
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.grass 1 557.78 619.78
- env.nontree 1 557.81 619.81
- env.bio_18 1 557.86 619.86
- env.bio_2 1 558.01 620.01
- env.nonveg 1 558.02 620.02
- env.bio_15 1 558.09 620.09
- env.bio_16 1 558.09 620.09
- env.bio_17 1 558.09 620.09
- env.elev 1 558.13 620.13
- env.bio_13 1 558.27 620.27
- env.bio_4 1 558.27 620.27
- env.bio_8 1 558.59 620.59
- env.bio_19 1 558.62 620.62
- env.woodysavanna 1 558.63 620.63
- env.barren 1 558.78 620.78
- env.wetland 1 559.06 621.06
- env.bio_14 1 559.33 621.33
- env.bio_10 1 559.33 621.33
- env.bio_12 1 559.40 621.40
<none> 557.73 621.73
- env.bio_11 1 560.30 622.30
- env.npp 1 561.50 623.50
- env.bio_6 1 561.84 623.84
- env.water 1 561.92 623.92
- env.savanna 1 561.93 623.93
- env.bio_3 1 562.12 624.12
- env.bio_5 1 562.54 624.54
- env.bio_7 1 564.78 626.78
- env.bio_9 1 565.05 627.05
- env.tree 1 565.14 627.14
- env.bio_1 1 568.28 630.28
- env.urban 1 568.42 630.42
Step: AIC=619.78
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.npp + env.tree + env.nontree + env.nonveg
Df Deviance AIC
- env.nontree 1 557.84 617.84
- env.bio_18 1 557.91 617.91
- env.nonveg 1 558.03 618.03
- env.bio_2 1 558.07 618.07
- env.bio_15 1 558.13 618.13
- env.bio_16 1 558.13 618.13
- env.bio_17 1 558.13 618.13
- env.elev 1 558.24 618.24
- env.bio_4 1 558.35 618.35
- env.bio_13 1 558.36 618.36
- env.bio_8 1 558.60 618.60
- env.woodysavanna 1 558.63 618.63
- env.bio_19 1 558.65 618.65
- env.barren 1 558.82 618.82
- env.wetland 1 559.08 619.08
- env.bio_10 1 559.38 619.38
- env.bio_14 1 559.45 619.45
- env.bio_12 1 559.45 619.45
<none> 557.78 619.78
- env.bio_11 1 560.30 620.30
- env.npp 1 561.65 621.65
- env.bio_6 1 561.84 621.84
- env.water 1 562.00 622.00
- env.bio_3 1 562.28 622.28
- env.bio_5 1 562.84 622.84
- env.savanna 1 563.48 623.48
- env.bio_7 1 564.78 624.78
- env.bio_9 1 565.07 625.07
- env.tree 1 566.75 626.75
- env.urban 1 568.43 628.43
- env.bio_1 1 568.56 628.56
Step: AIC=617.84
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.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_18 1 557.97 615.97
- env.bio_2 1 558.14 616.14
- env.bio_15 1 558.19 616.19
- env.bio_16 1 558.19 616.19
- env.bio_17 1 558.19 616.19
- env.elev 1 558.25 616.25
- env.bio_13 1 558.42 616.42
- env.bio_4 1 558.45 616.45
- env.woodysavanna 1 558.68 616.68
- env.bio_8 1 558.69 616.69
- env.bio_19 1 558.78 616.78
- env.barren 1 558.92 616.92
- env.wetland 1 559.08 617.08
- env.bio_10 1 559.39 617.39
- env.bio_12 1 559.45 617.45
- env.nonveg 1 559.67 617.67
- env.bio_14 1 559.79 617.79
<none> 557.84 617.84
- env.bio_11 1 560.30 618.30
- env.npp 1 561.77 619.77
- env.bio_6 1 561.93 619.93
- env.bio_3 1 562.89 620.89
- env.bio_5 1 563.26 621.26
- env.water 1 563.27 621.27
- env.savanna 1 563.48 621.48
- env.bio_7 1 565.02 623.02
- env.bio_9 1 565.11 623.11
- env.urban 1 568.68 626.68
- env.bio_1 1 569.23 627.23
- env.tree 1 584.68 642.68
Step: AIC=615.97
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_19 + env.elev + env.urban +
env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_2 1 558.27 614.27
- env.bio_15 1 558.30 614.30
- env.bio_16 1 558.30 614.30
- env.bio_17 1 558.30 614.30
- env.elev 1 558.38 614.38
- env.bio_13 1 558.54 614.54
- env.bio_4 1 558.54 614.54
- env.woodysavanna 1 558.81 614.81
- env.bio_8 1 558.85 614.85
- env.barren 1 559.05 615.05
- env.wetland 1 559.19 615.19
- env.bio_10 1 559.40 615.40
- env.bio_12 1 559.58 615.58
- env.bio_19 1 559.78 615.78
- env.nonveg 1 559.90 615.90
- env.bio_14 1 559.92 615.92
<none> 557.97 615.97
- env.bio_11 1 560.40 616.40
- env.npp 1 561.78 617.78
- env.bio_6 1 561.98 617.98
- env.bio_3 1 563.05 619.05
- env.water 1 563.39 619.39
- env.bio_5 1 563.41 619.41
- env.savanna 1 563.66 619.66
- env.bio_9 1 565.15 621.15
- env.bio_7 1 565.15 621.15
- env.urban 1 568.80 624.80
- env.bio_1 1 569.77 625.77
- env.tree 1 585.57 641.57
Step: AIC=614.27
presabs ~ env.bio_1 + 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_19 + env.elev + env.urban + env.barren +
env.water + env.savanna + env.woodysavanna + env.wetland +
env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.elev 1 558.58 612.58
- env.bio_15 1 558.66 612.66
- env.bio_16 1 558.66 612.66
- env.bio_17 1 558.66 612.66
- env.bio_4 1 558.75 612.75
- env.bio_13 1 558.85 612.85
- env.woodysavanna 1 559.24 613.24
- env.bio_8 1 559.30 613.30
- env.wetland 1 559.34 613.34
- env.barren 1 559.37 613.37
- env.bio_10 1 559.62 613.62
- env.bio_12 1 559.76 613.76
- env.nonveg 1 560.16 614.16
<none> 558.27 614.27
- env.bio_11 1 560.53 614.53
- env.bio_19 1 560.53 614.53
- env.bio_14 1 560.64 614.64
- env.npp 1 562.72 616.72
- env.bio_6 1 562.92 616.92
- env.bio_3 1 563.81 617.81
- env.water 1 563.86 617.86
- env.savanna 1 563.90 617.90
- env.bio_5 1 564.22 618.22
- env.bio_7 1 566.70 620.70
- env.bio_9 1 566.93 620.93
- env.urban 1 571.40 625.40
- env.bio_1 1 572.11 626.11
- env.tree 1 589.60 643.60
Step: AIC=612.58
presabs ~ env.bio_1 + 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_19 + env.urban + env.barren + env.water +
env.savanna + env.woodysavanna + env.wetland + env.npp +
env.tree + env.nonveg
Df Deviance AIC
- env.bio_15 1 558.99 610.99
- env.bio_16 1 558.99 610.99
- env.bio_17 1 558.99 610.99
- env.bio_4 1 559.01 611.01
- env.bio_13 1 559.10 611.10
- env.woodysavanna 1 559.46 611.46
- env.barren 1 559.71 611.71
- env.wetland 1 559.77 611.77
- env.bio_8 1 559.82 611.82
- env.bio_10 1 560.15 612.15
- env.bio_12 1 560.17 612.17
<none> 558.58 612.58
- env.nonveg 1 560.69 612.69
- env.bio_11 1 560.84 612.84
- env.bio_19 1 560.93 612.93
- env.bio_14 1 561.76 613.76
- env.npp 1 562.72 614.72
- env.bio_6 1 563.09 615.09
- env.water 1 563.94 615.94
- env.savanna 1 564.12 616.12
- env.bio_5 1 565.28 617.28
- env.bio_3 1 565.29 617.29
- env.bio_9 1 567.17 619.17
- env.bio_7 1 567.72 619.72
- env.urban 1 571.51 623.51
- env.bio_1 1 573.31 625.31
- env.tree 1 589.68 641.68
Step: AIC=610.99
presabs ~ env.bio_1 + 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_16 + env.bio_17 +
env.bio_19 + env.urban + env.barren + env.water + env.savanna +
env.woodysavanna + env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_16 1 559.40 609.40
- env.bio_4 1 559.48 609.48
- env.bio_13 1 559.60 609.60
- env.woodysavanna 1 559.99 609.99
- env.bio_8 1 560.21 610.21
- env.wetland 1 560.21 610.21
- env.barren 1 560.23 610.23
- env.bio_10 1 560.56 610.56
- env.bio_12 1 560.73 610.73
<none> 558.99 610.99
- env.nonveg 1 561.10 611.10
- env.bio_11 1 561.30 611.30
- env.bio_19 1 561.41 611.41
- env.bio_14 1 562.27 612.27
- env.bio_17 1 562.85 612.85
- env.npp 1 563.02 613.02
- env.bio_6 1 563.54 613.54
- env.water 1 564.49 614.49
- env.savanna 1 564.66 614.66
- env.bio_5 1 565.90 615.90
- env.bio_3 1 565.97 615.97
- env.bio_9 1 567.63 617.63
- env.bio_7 1 568.59 618.59
- env.urban 1 571.61 621.61
- env.bio_1 1 573.90 623.90
- env.tree 1 589.94 639.94
Step: AIC=609.4
presabs ~ env.bio_1 + 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_17 + env.bio_19 +
env.urban + env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_4 1 559.92 607.92
- env.woodysavanna 1 560.25 608.25
- env.bio_13 1 560.26 608.26
- env.wetland 1 560.36 608.36
- env.bio_8 1 560.67 608.67
- env.bio_10 1 560.72 608.72
- env.barren 1 560.77 608.77
- env.nonveg 1 561.40 609.40
<none> 559.40 609.40
- env.bio_12 1 561.60 609.60
- env.bio_11 1 561.99 609.99
- env.bio_17 1 562.86 610.86
- env.npp 1 563.02 611.02
- env.bio_19 1 563.34 611.34
- env.bio_6 1 564.69 612.69
- env.savanna 1 564.91 612.91
- env.water 1 565.01 613.01
- env.bio_14 1 565.71 613.71
- env.bio_5 1 566.37 614.37
- env.bio_9 1 568.90 616.90
- env.bio_7 1 569.44 617.44
- env.urban 1 571.74 619.74
- env.bio_3 1 574.85 622.85
- env.bio_1 1 577.19 625.19
- env.tree 1 590.01 638.01
Step: AIC=607.92
presabs ~ env.bio_1 + env.bio_3 + 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_17 + env.bio_19 + env.urban +
env.barren + env.water + env.savanna + env.woodysavanna +
env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_13 1 560.50 606.50
- env.woodysavanna 1 560.59 606.59
- env.wetland 1 560.87 606.87
- env.bio_10 1 560.91 606.91
- env.barren 1 561.27 607.27
- env.bio_12 1 561.60 607.60
<none> 559.92 607.92
- env.nonveg 1 562.19 608.19
- env.bio_11 1 562.73 608.73
- env.bio_17 1 562.96 608.96
- env.npp 1 563.18 609.18
- env.bio_19 1 563.65 609.65
- env.bio_8 1 564.21 610.21
- env.savanna 1 565.10 611.10
- env.water 1 565.84 611.84
- env.bio_6 1 566.17 612.17
- env.bio_5 1 566.61 612.61
- env.bio_14 1 567.26 613.26
- env.urban 1 572.13 618.13
- env.bio_7 1 572.24 618.24
- env.bio_9 1 573.17 619.17
- env.bio_3 1 575.00 621.00
- env.bio_1 1 577.29 623.29
- env.tree 1 593.50 639.50
Step: AIC=606.5
presabs ~ env.bio_1 + env.bio_3 + 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_14 + env.bio_17 + env.bio_19 + env.urban + env.barren +
env.water + env.savanna + env.woodysavanna + env.wetland +
env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.woodysavanna 1 561.28 605.28
- env.bio_10 1 561.49 605.49
- env.bio_12 1 561.76 605.76
- env.barren 1 561.88 605.88
- env.wetland 1 561.89 605.89
<none> 560.50 606.50
- env.nonveg 1 563.54 607.54
- env.bio_17 1 563.66 607.66
- env.npp 1 563.70 607.70
- env.bio_19 1 563.92 607.92
- env.bio_8 1 564.55 608.55
- env.bio_11 1 564.66 608.66
- env.water 1 566.20 610.20
- env.bio_5 1 566.75 610.75
- env.bio_6 1 566.83 610.83
- env.savanna 1 566.93 610.93
- env.bio_14 1 568.05 612.05
- env.bio_9 1 573.24 617.24
- env.urban 1 573.56 617.56
- env.bio_7 1 573.62 617.62
- env.bio_3 1 576.18 620.18
- env.bio_1 1 578.15 622.15
- env.tree 1 597.54 641.54
Step: AIC=605.28
presabs ~ env.bio_1 + env.bio_3 + 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_14 + env.bio_17 + env.bio_19 + env.urban + env.barren +
env.water + env.savanna + env.wetland + env.npp + env.tree +
env.nonveg
Df Deviance AIC
- env.bio_10 1 562.18 604.18
- env.wetland 1 562.39 604.39
- env.bio_12 1 562.57 604.57
- env.barren 1 562.66 604.66
<none> 561.28 605.28
- env.npp 1 564.22 606.22
- env.nonveg 1 564.26 606.26
- env.bio_17 1 564.50 606.50
- env.bio_19 1 564.68 606.68
- env.bio_11 1 565.18 607.18
- env.bio_8 1 565.85 607.85
- env.savanna 1 566.99 608.99
- env.water 1 567.35 609.35
- env.bio_6 1 567.47 609.47
- env.bio_5 1 568.67 610.67
- env.bio_14 1 568.77 610.77
- env.bio_9 1 573.89 615.89
- env.urban 1 574.14 616.14
- env.bio_7 1 575.78 617.78
- env.bio_3 1 576.89 618.89
- env.bio_1 1 578.87 620.87
- env.tree 1 598.16 640.16
Step: AIC=604.18
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_9 + env.bio_11 + env.bio_12 + env.bio_14 +
env.bio_17 + env.bio_19 + env.urban + env.barren + env.water +
env.savanna + env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.bio_12 1 562.90 602.90
- env.wetland 1 563.41 603.41
- env.barren 1 563.67 603.67
<none> 562.18 604.18
- env.bio_17 1 564.51 604.51
- env.bio_19 1 564.86 604.86
- env.nonveg 1 565.00 605.00
- env.npp 1 565.19 605.19
- env.bio_11 1 565.43 605.43
- env.bio_8 1 565.88 605.88
- env.savanna 1 567.71 607.71
- env.water 1 568.03 608.03
- env.bio_6 1 568.04 608.04
- env.bio_5 1 569.09 609.09
- env.bio_14 1 569.41 609.41
- env.bio_9 1 573.96 613.96
- env.urban 1 574.15 614.15
- env.bio_7 1 576.76 616.76
- env.bio_3 1 577.04 617.04
- env.bio_1 1 579.17 619.17
- env.tree 1 598.56 638.56
Step: AIC=602.9
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_9 + env.bio_11 + env.bio_14 + env.bio_17 +
env.bio_19 + env.urban + env.barren + env.water + env.savanna +
env.wetland + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.wetland 1 564.11 602.11
- env.barren 1 564.35 602.35
<none> 562.90 602.90
- env.bio_11 1 565.74 603.74
- env.nonveg 1 565.80 603.80
- env.bio_8 1 565.91 603.91
- env.npp 1 566.01 604.01
- env.bio_19 1 566.34 604.34
- env.bio_17 1 567.99 605.99
- env.water 1 568.48 606.48
- env.bio_5 1 569.14 607.14
- env.bio_6 1 569.48 607.48
- env.savanna 1 570.14 608.14
- env.urban 1 575.24 613.24
- env.bio_9 1 577.60 615.60
- env.bio_14 1 582.04 620.04
- env.bio_3 1 586.14 624.14
- env.bio_7 1 587.06 625.06
- env.bio_1 1 590.17 628.17
- env.tree 1 598.56 636.56
Step: AIC=602.11
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_9 + env.bio_11 + env.bio_14 + env.bio_17 +
env.bio_19 + env.urban + env.barren + env.water + env.savanna +
env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.barren 1 565.83 601.83
<none> 564.11 602.11
- env.nonveg 1 566.28 602.28
- env.bio_8 1 566.44 602.44
- env.npp 1 566.49 602.49
- env.bio_11 1 567.38 603.38
- env.bio_19 1 567.44 603.44
- env.bio_5 1 569.63 605.63
- env.savanna 1 570.52 606.52
- env.bio_6 1 571.34 607.34
- env.bio_17 1 571.50 607.50
- env.water 1 572.89 608.89
- env.urban 1 576.19 612.19
- env.bio_9 1 579.58 615.58
- env.bio_14 1 582.44 618.44
- env.bio_3 1 586.62 622.62
- env.bio_7 1 588.47 624.47
- env.bio_1 1 590.54 626.54
- env.tree 1 602.53 638.53
Step: AIC=601.83
presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_9 + env.bio_11 + env.bio_14 + env.bio_17 +
env.bio_19 + env.urban + env.water + env.savanna + env.npp +
env.tree + env.nonveg
Df Deviance AIC
<none> 565.83 601.83
- env.bio_8 1 567.95 601.95
- env.npp 1 568.36 602.36
- env.nonveg 1 568.45 602.45
- env.bio_11 1 569.01 603.01
- env.bio_19 1 569.28 603.28
- env.bio_5 1 571.10 605.10
- env.savanna 1 572.44 606.44
- env.bio_6 1 573.12 607.12
- env.bio_17 1 573.27 607.27
- env.water 1 573.72 607.72
- env.urban 1 578.09 612.09
- env.bio_9 1 581.56 615.56
- env.bio_14 1 584.49 618.49
- env.bio_3 1 588.81 622.81
- env.bio_7 1 590.87 624.87
- env.bio_1 1 592.93 626.93
- env.tree 1 603.56 637.56
Call: glm(formula = presabs ~ env.bio_1 + env.bio_3 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_8 + env.bio_9 + env.bio_11 + env.bio_14 +
env.bio_17 + env.bio_19 + env.urban + env.water + env.savanna +
env.npp + env.tree + env.nonveg, family = "binomial", data = presabs.glm)
Coefficients:
(Intercept) env.bio_1 env.bio_3 env.bio_5 env.bio_6 env.bio_7
-1.3898 27.0023 -32.3473 -2.9027 -3.9710 1.8965
env.bio_8 env.bio_9 env.bio_11 env.bio_14 env.bio_17 env.bio_19
1.9400 6.5682 -0.6498 -11.7316 -1.4761 -1.6537
env.urban env.water env.savanna env.npp env.tree env.nonveg
0.6775 -0.4390 0.3737 0.3869 1.3845 -0.4200
Degrees of Freedom: 558 Total (i.e. Null); 541 Residual
Null Deviance: 774.9
Residual Deviance: 565.8 AIC: 601.8Boosted 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 = 1.3863
tolerance is fixed at 0.0014
ntrees resid. dev.
50 1.2935
now adding trees...
100 1.2351
150 1.1961
200 1.1672
250 1.1471
300 1.1318
350 1.1187
400 1.1073
450 1.0983
500 1.0903
550 1.0819
600 1.0755
650 1.0688
700 1.0634
750 1.0581
800 1.0542
850 1.0509
900 1.0455
950 1.0423
1000 1.0391
1050 1.0364
1100 1.0328
1150 1.0298
1200 1.0279
1250 1.0253
1300 1.0223
1350 1.0208
1400 1.0187
1450 1.0172
1500 1.0161
1550 1.0143
1600 1.0119
1650 1.0096
1700 1.008
1750 1.0055
1800 1.0034
1850 1.0022
1900 1.0007
1950 0.9995
2000 0.9988
2050 0.9975
2100 0.9956
2150 0.9952
2200 0.9935
2250 0.9912
2300 0.9902
2350 0.9898
2400 0.9882
2450 0.9871
2500 0.9863
2550 0.9861
2600 0.9848
2650 0.9843
2700 0.9827
2750 0.983
2800 0.9833
2850 0.9832
2900 0.9834
2950 0.9827
3000 0.9826
3050 0.9818
3100 0.9814
3150 0.9811
3200 0.9801
3250 0.9801
3300 0.9793
3350 0.9789
3400 0.9786
3450 0.9783
3500 0.9772
3550 0.977
3600 0.9759
3650 0.975
3700 0.9757
3750 0.9754
3800 0.9749
3850 0.9745
3900 0.9731
3950 0.9734
4000 0.9732
4050 0.9734
4100 0.9734
4150 0.9728
4200 0.9718
4250 0.9711
4300 0.9704
4350 0.9705
4400 0.9705
4450 0.97
4500 0.9714
4550 0.9714
4600 0.971
4650 0.9706
4700 0.9701
4750 0.9688
4800 0.9692
4850 0.9695
4900 0.97
4950 0.97
5000 0.9695
5050 0.9683
5100 0.9681
5150 0.9683
5200 0.9682
5250 0.9683
5300 0.9683
5350 0.9698 
mean total deviance = 1.386
mean residual deviance = 0.571
estimated cv deviance = 0.968 ; se = 0.059
training data correlation = 0.834
cv correlation = 0.608 ; se = 0.031
training data AUC score = 0.968
cv AUC score = 0.853 ; se = 0.017
elapsed time - 0.28 minutes Code
summary(brt)
var rel.inf
env.tree env.tree 11.7302894
env.elev env.elev 9.6817742
env.bio_17 env.bio_17 8.8562332
env.woodysavanna env.woodysavanna 7.1180112
env.npp env.npp 6.2798139
env.bio_15 env.bio_15 5.0728898
env.bio_14 env.bio_14 4.3614461
env.bio_2 env.bio_2 4.2681246
env.bio_18 env.bio_18 4.1670428
env.bio_10 env.bio_10 3.4701908
env.bio_12 env.bio_12 3.4565897
env.urban env.urban 3.3790913
env.bio_16 env.bio_16 3.1930778
env.bio_3 env.bio_3 2.7436299
env.bio_13 env.bio_13 2.5993996
env.nonveg env.nonveg 2.5805637
env.bio_4 env.bio_4 2.4733848
env.bio_6 env.bio_6 2.1168927
env.bio_1 env.bio_1 1.8952839
env.wetland env.wetland 1.7401165
env.bio_19 env.bio_19 1.6302436
env.bio_5 env.bio_5 1.5280400
env.bio_7 env.bio_7 1.2361423
env.nontree env.nontree 1.2080348
env.bio_9 env.bio_9 1.0556385
env.bio_11 env.bio_11 0.8465173
env.bio_8 env.bio_8 0.5908027
env.grass env.grass 0.4701150
env.savanna env.savanna 0.1379277
env.water env.water 0.1126921
env.barren env.barren 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
8.00994512 5.69730445 9.20756839 6.98218133
env.bio_5 env.bio_6 env.bio_7 env.bio_8
6.44026833 5.50565207 7.51740335 6.60696112
env.bio_9 env.bio_10 env.bio_11 env.bio_12
5.56641720 9.48983741 6.10012272 10.61931424
env.bio_13 env.bio_14 env.bio_15 env.bio_16
9.28749100 10.35968089 7.49871714 9.51423830
env.bio_17 env.bio_18 env.bio_19 env.elev
21.54346953 7.33078318 7.16331196 9.06160369
env.urban env.barren env.water env.savanna
7.67734814 0.04048197 1.23154395 4.56015519
env.woodysavanna env.wetland env.grass env.npp
12.65623044 2.80931440 7.04236310 15.54416686
env.tree env.nontree env.nonveg
25.52167555 10.53532707 8.57131990 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.tree, env.elev, env.bio_17, env.woodysavanna, env.npp, env.bio_15
- Random forest: env.tree, env.bio_17, env.npp, env.bio_14, env.nontree, env.bio_12
- Ranger: env.tree, env.bio_17, env.npp, env.woodysavanna, env.bio_12, env.nontree
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.tree | env.elev | env.bio_17 | env.woodysavanna | env.npp | env.bio_15 | env.bio_14 | env.nontree | env.bio_12 | |
|---|---|---|---|---|---|---|---|---|---|
| env.tree | 1.0000000 | -0.3968661 | -0.4936629 | -0.0777231 | -0.0468240 | 0.0503882 | -0.1926304 | 0.2718941 | -0.5951082 |
| env.elev | -0.3968661 | 1.0000000 | 0.3091761 | 0.0062860 | 0.3623028 | -0.6630353 | 0.1075528 | -0.0874815 | 0.3821173 |
| env.bio_17 | -0.4936629 | 0.3091761 | 1.0000000 | 0.0293186 | -0.0198950 | -0.0543381 | 0.7896644 | -0.1095460 | 0.8023981 |
| env.woodysavanna | -0.0777231 | 0.0062860 | 0.0293186 | 1.0000000 | 0.0006886 | 0.0462821 | 0.0098816 | -0.1006558 | 0.0467406 |
| env.npp | -0.0468240 | 0.3623028 | -0.0198950 | 0.0006886 | 1.0000000 | -0.6102193 | 0.1143688 | -0.5293037 | -0.1737652 |
| env.bio_15 | 0.0503882 | -0.6630353 | -0.0543381 | 0.0462821 | -0.6102193 | 1.0000000 | -0.3065325 | 0.1630034 | 0.1966475 |
| env.bio_14 | -0.1926304 | 0.1075528 | 0.7896644 | 0.0098816 | 0.1143688 | -0.3065325 | 1.0000000 | -0.0852376 | 0.3049036 |
| env.nontree | 0.2718941 | -0.0874815 | -0.1095460 | -0.1006558 | -0.5293037 | 0.1630034 | -0.0852376 | 1.0000000 | -0.0668483 |
| env.bio_12 | -0.5951082 | 0.3821173 | 0.8023981 | 0.0467406 | -0.1737652 | 0.1966475 | 0.3049036 | -0.0668483 | 1.0000000 |
Code
tmpFiles(remove=T)