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)Herpailurus yagouaroundi Variable Selection
Variable Selection for Herpailurus yagouaroundi, 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()Herpailurus yagouaroundi’ preferences
We will use the data from both periods
Code
PA_time1 <- readRDS('data/species_POPA_data/PA_hyagouaroundi_time1_blobs.rds')%>% ungroup()
PA_time2 <- readRDS('data/species_POPA_data/PA_hyagouaroundi_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 | 0 | 1769 days | 1779 | 756798182 [m^2] |
| 6 | 1 | 304 days | 3300 | 19990863 [m^2] |
Code
PA_time2 %>% st_drop_geometry() %>% head() %>% kable()| ID | presence | temporalSpan | effort | blobArea |
|---|---|---|---|---|
| 1 | 0 | 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
-3.0044 -0.5718 -0.3831 0.4691 2.5916
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.074e+02 1.377e+02 -0.780 0.435413
env.bio_1 3.619e+00 7.514e+00 0.482 0.630079
env.bio_2 1.475e+01 8.510e+00 1.734 0.082963 .
env.bio_3 -2.370e+01 1.431e+01 -1.656 0.097784 .
env.bio_4 2.236e+00 1.827e+00 1.224 0.221006
env.bio_5 -2.494e+00 1.582e+00 -1.576 0.115064
env.bio_6 -2.121e+00 1.565e+00 -1.355 0.175417
env.bio_7 9.541e-01 5.486e-01 1.739 0.082017 .
env.bio_8 1.763e+00 2.123e+00 0.830 0.406267
env.bio_9 2.320e+00 2.008e+00 1.155 0.247959
env.bio_10 -4.717e-01 4.438e-01 -1.063 0.287782
env.bio_11 -1.199e+00 6.246e-01 -1.920 0.054832 .
env.bio_12 8.862e+00 2.041e+00 4.342 1.41e-05 ***
env.bio_13 -2.514e+00 1.310e+00 -1.919 0.054971 .
env.bio_14 -5.102e+00 8.250e+00 -0.618 0.536297
env.bio_15 -1.318e+06 1.220e+06 -1.080 0.280105
env.bio_16 2.209e+06 2.045e+06 1.080 0.280105
env.bio_17 1.877e+06 1.738e+06 1.080 0.280109
env.bio_18 -5.898e-02 9.893e-01 -0.060 0.952459
env.bio_19 1.760e+00 1.178e+00 1.493 0.135399
env.elev 9.756e-01 9.652e-01 1.011 0.312122
env.urban 1.109e+00 2.924e-01 3.794 0.000148 ***
env.barren -5.340e+02 6.949e+02 -0.768 0.442220
env.water -1.892e-01 3.087e-01 -0.613 0.540058
env.savanna -1.701e-01 2.392e-01 -0.711 0.477059
env.woodysavanna 4.208e-01 2.585e-01 1.628 0.103452
env.wetland -1.179e-01 1.614e-01 -0.730 0.465351
env.grass -4.654e-01 3.519e-01 -1.323 0.185952
env.npp 1.078e+00 3.402e-01 3.170 0.001526 **
env.tree 7.606e-02 5.639e-01 0.135 0.892698
env.nontree -2.874e-02 5.507e-01 -0.052 0.958382
env.nonveg 2.627e-01 6.971e-01 0.377 0.706228
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 650.23 on 558 degrees of freedom
Residual deviance: 440.38 on 527 degrees of freedom
AIC: 504.38
Number of Fisher Scoring iterations: 14Code
step(glm.full) # step might not work with gam so glmStart: AIC=504.38
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.nontree 1 440.38 502.38
- env.bio_18 1 440.38 502.38
- env.tree 1 440.40 502.40
- env.nonveg 1 440.52 502.52
- env.bio_1 1 440.61 502.61
- env.water 1 440.76 502.76
- env.bio_14 1 440.77 502.77
- env.savanna 1 440.89 502.89
- env.wetland 1 441.01 503.01
- env.bio_8 1 441.07 503.07
- env.elev 1 441.44 503.44
- env.bio_10 1 441.53 503.53
- env.bio_17 1 441.55 503.55
- env.bio_16 1 441.55 503.55
- env.bio_15 1 441.55 503.55
- env.barren 1 441.69 503.69
- env.bio_9 1 441.71 503.71
- env.bio_4 1 441.90 503.90
- env.grass 1 442.17 504.17
- env.bio_6 1 442.20 504.20
<none> 440.38 504.38
- env.bio_19 1 442.53 504.53
- env.bio_5 1 442.88 504.88
- env.woodysavanna 1 443.05 505.05
- env.bio_3 1 443.15 505.15
- env.bio_7 1 443.51 505.51
- env.bio_2 1 443.58 505.58
- env.bio_13 1 444.15 506.15
- env.bio_11 1 444.43 506.43
- env.npp 1 451.91 513.91
- env.urban 1 459.64 521.64
- env.bio_12 1 462.57 524.57
Step: AIC=502.38
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.nonveg
Df Deviance AIC
- env.bio_18 1 440.39 500.39
- env.tree 1 440.44 500.44
- env.bio_1 1 440.62 500.62
- env.nonveg 1 440.69 500.69
- env.bio_14 1 440.79 500.79
- env.water 1 440.86 500.86
- env.savanna 1 440.91 500.91
- env.bio_8 1 441.07 501.07
- env.wetland 1 441.07 501.07
- env.elev 1 441.47 501.47
- env.bio_10 1 441.53 501.53
- env.bio_17 1 441.55 501.55
- env.bio_16 1 441.55 501.55
- env.bio_15 1 441.55 501.55
- env.bio_9 1 441.71 501.71
- env.barren 1 441.74 501.74
- env.bio_4 1 441.91 501.91
- env.grass 1 442.20 502.20
- env.bio_6 1 442.22 502.22
<none> 440.38 502.38
- env.bio_19 1 442.54 502.54
- env.bio_5 1 442.89 502.89
- env.woodysavanna 1 443.05 503.05
- env.bio_3 1 443.19 503.19
- env.bio_7 1 443.53 503.53
- env.bio_2 1 443.58 503.58
- env.bio_13 1 444.16 504.16
- env.bio_11 1 444.50 504.50
- env.npp 1 452.67 512.67
- env.urban 1 460.34 520.34
- env.bio_12 1 463.65 523.65
Step: AIC=500.39
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.grass + env.npp + env.tree + env.nonveg
Df Deviance AIC
- env.tree 1 440.45 498.45
- env.bio_1 1 440.62 498.62
- env.nonveg 1 440.70 498.70
- env.bio_14 1 440.79 498.79
- env.water 1 440.86 498.86
- env.savanna 1 440.92 498.92
- env.wetland 1 441.07 499.07
- env.bio_8 1 441.07 499.07
- env.elev 1 441.47 499.47
- env.bio_17 1 441.56 499.56
- env.bio_16 1 441.56 499.56
- env.bio_15 1 441.56 499.56
- env.bio_10 1 441.61 499.61
- env.barren 1 441.74 499.74
- env.bio_9 1 441.75 499.75
- env.bio_4 1 441.91 499.91
- env.grass 1 442.21 500.21
- env.bio_6 1 442.27 500.27
<none> 440.39 500.39
- env.bio_5 1 442.89 500.89
- env.woodysavanna 1 443.06 501.06
- env.bio_3 1 443.19 501.19
- env.bio_7 1 443.53 501.53
- env.bio_2 1 443.61 501.61
- env.bio_19 1 443.88 501.88
- env.bio_13 1 444.16 502.16
- env.bio_11 1 444.53 502.53
- env.npp 1 452.75 510.75
- env.urban 1 460.34 518.34
- env.bio_12 1 463.67 521.67
Step: AIC=498.45
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.grass + env.npp + env.nonveg
Df Deviance AIC
- env.bio_1 1 440.70 496.70
- env.water 1 440.89 496.89
- env.bio_14 1 440.93 496.93
- env.nonveg 1 441.06 497.06
- env.bio_8 1 441.07 497.07
- env.wetland 1 441.17 497.17
- env.savanna 1 441.61 497.61
- env.elev 1 441.61 497.61
- env.bio_17 1 441.65 497.65
- env.bio_16 1 441.65 497.65
- env.bio_15 1 441.65 497.65
- env.bio_10 1 441.71 497.71
- env.bio_9 1 441.76 497.76
- env.barren 1 441.85 497.85
- env.bio_4 1 442.18 498.18
- env.bio_6 1 442.28 498.28
<none> 440.45 498.45
- env.bio_5 1 442.90 498.90
- env.woodysavanna 1 443.07 499.07
- env.bio_3 1 443.55 499.55
- env.bio_7 1 443.66 499.66
- env.bio_19 1 443.88 499.88
- env.bio_2 1 443.90 499.90
- env.bio_11 1 444.53 500.53
- env.grass 1 444.67 500.67
- env.bio_13 1 445.23 501.23
- env.npp 1 453.64 509.64
- env.urban 1 460.37 516.37
- env.bio_12 1 465.17 521.17
Step: AIC=496.7
presabs ~ 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.grass + env.npp + env.nonveg
Df Deviance AIC
- env.bio_14 1 441.02 495.02
- env.water 1 441.15 495.15
- env.nonveg 1 441.22 495.22
- env.bio_8 1 441.28 495.28
- env.wetland 1 441.36 495.36
- env.bio_9 1 441.78 495.78
- env.bio_10 1 441.79 495.79
- env.bio_17 1 441.89 495.89
- env.bio_16 1 441.89 495.89
- env.bio_15 1 441.89 495.89
- env.barren 1 442.01 496.01
- env.elev 1 442.14 496.14
- env.bio_6 1 442.32 496.32
- env.savanna 1 442.33 496.33
- env.bio_4 1 442.63 496.63
<none> 440.70 496.70
- env.bio_5 1 443.09 497.09
- env.woodysavanna 1 443.39 497.39
- env.bio_7 1 443.68 497.68
- env.bio_3 1 443.91 497.91
- env.bio_11 1 444.72 498.72
- env.grass 1 444.74 498.74
- env.bio_19 1 445.02 499.02
- env.bio_2 1 445.02 499.02
- env.bio_13 1 445.71 499.71
- env.npp 1 453.82 507.82
- env.urban 1 461.53 515.53
- env.bio_12 1 468.88 522.88
Step: AIC=495.02
presabs ~ 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_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.grass +
env.npp + env.nonveg
Df Deviance AIC
- env.water 1 441.48 493.48
- env.bio_8 1 441.62 493.62
- env.nonveg 1 441.65 493.65
- env.wetland 1 441.73 493.73
- env.bio_9 1 442.14 494.14
- env.bio_17 1 442.14 494.14
- env.bio_16 1 442.14 494.14
- env.bio_15 1 442.14 494.14
- env.bio_10 1 442.18 494.18
- env.barren 1 442.44 494.44
- env.elev 1 442.47 494.47
- env.bio_6 1 442.66 494.66
- env.savanna 1 442.78 494.78
- env.bio_4 1 443.00 495.00
<none> 441.02 495.02
- env.bio_5 1 443.58 495.58
- env.woodysavanna 1 444.03 496.03
- env.bio_7 1 444.44 496.44
- env.bio_11 1 444.92 496.92
- env.bio_19 1 445.21 497.21
- env.grass 1 445.37 497.37
- env.bio_13 1 446.07 498.07
- env.bio_3 1 454.67 506.67
- env.npp 1 458.01 510.01
- env.bio_2 1 464.91 516.91
- env.urban 1 468.48 520.48
- env.bio_12 1 469.81 521.81
Step: AIC=493.48
presabs ~ 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_15 + env.bio_16 + env.bio_17 +
env.bio_19 + env.elev + env.urban + env.barren + env.savanna +
env.woodysavanna + env.wetland + env.grass + env.npp + env.nonveg
Df Deviance AIC
- env.nonveg 1 441.65 491.65
- env.wetland 1 441.92 491.92
- env.bio_8 1 442.07 492.07
- env.bio_9 1 442.53 492.53
- env.bio_17 1 442.54 492.54
- env.bio_16 1 442.54 492.54
- env.bio_15 1 442.54 492.54
- env.barren 1 442.68 492.68
- env.bio_10 1 442.70 492.70
- env.elev 1 442.72 492.72
- env.bio_6 1 443.01 493.01
- env.savanna 1 443.07 493.07
- env.bio_4 1 443.40 493.40
<none> 441.48 493.48
- env.bio_5 1 443.93 493.93
- env.woodysavanna 1 444.65 494.65
- env.bio_7 1 444.73 494.73
- env.bio_11 1 445.54 495.54
- env.grass 1 445.56 495.56
- env.bio_19 1 445.71 495.71
- env.bio_13 1 446.18 496.18
- env.bio_3 1 454.72 504.72
- env.npp 1 458.71 508.71
- env.bio_2 1 465.17 515.17
- env.urban 1 468.99 518.99
- env.bio_12 1 469.81 519.81
Step: AIC=491.65
presabs ~ 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_15 + env.bio_16 + env.bio_17 +
env.bio_19 + env.elev + env.urban + env.barren + env.savanna +
env.woodysavanna + env.wetland + env.grass + env.npp
Df Deviance AIC
- env.wetland 1 442.01 490.01
- env.bio_8 1 442.41 490.41
- env.bio_17 1 442.78 490.78
- env.bio_16 1 442.78 490.78
- env.bio_15 1 442.78 490.78
- env.barren 1 442.85 490.85
- env.elev 1 442.86 490.86
- env.bio_9 1 442.95 490.95
- env.bio_10 1 443.19 491.19
- env.bio_6 1 443.32 491.32
- env.savanna 1 443.38 491.38
- env.bio_4 1 443.41 491.41
<none> 441.65 491.65
- env.bio_5 1 444.11 492.11
- env.woodysavanna 1 444.72 492.72
- env.bio_7 1 444.86 492.86
- env.bio_19 1 445.75 493.75
- env.grass 1 445.81 493.81
- env.bio_13 1 446.18 494.18
- env.bio_11 1 446.47 494.47
- env.bio_3 1 454.87 502.87
- env.npp 1 464.13 512.13
- env.bio_2 1 465.20 513.20
- env.urban 1 469.01 517.01
- env.bio_12 1 470.12 518.12
Step: AIC=490.01
presabs ~ 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_15 + env.bio_16 + env.bio_17 +
env.bio_19 + env.elev + env.urban + env.barren + env.savanna +
env.woodysavanna + env.grass + env.npp
Df Deviance AIC
- env.bio_8 1 442.88 488.88
- env.elev 1 443.09 489.09
- env.barren 1 443.16 489.16
- env.bio_17 1 443.18 489.18
- env.bio_16 1 443.18 489.18
- env.bio_15 1 443.18 489.18
- env.bio_9 1 443.21 489.21
- env.savanna 1 443.44 489.44
- env.bio_10 1 443.57 489.57
- env.bio_6 1 443.59 489.59
- env.bio_4 1 443.78 489.78
<none> 442.01 490.01
- env.bio_5 1 444.68 490.68
- env.bio_7 1 445.05 491.05
- env.woodysavanna 1 445.62 491.62
- env.grass 1 445.86 491.86
- env.bio_13 1 446.27 492.27
- env.bio_19 1 446.39 492.39
- env.bio_11 1 446.66 492.66
- env.bio_3 1 454.88 500.88
- env.bio_2 1 466.26 512.26
- env.bio_12 1 470.12 516.12
- env.urban 1 470.54 516.54
- env.npp 1 473.00 519.00
Step: AIC=488.88
presabs ~ 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_15 + env.bio_16 + env.bio_17 + env.bio_19 +
env.elev + env.urban + env.barren + env.savanna + env.woodysavanna +
env.grass + env.npp
Df Deviance AIC
- env.bio_9 1 443.74 487.74
- env.savanna 1 443.94 487.94
- env.barren 1 443.97 487.97
- env.bio_17 1 444.22 488.22
- env.bio_16 1 444.22 488.22
- env.bio_15 1 444.22 488.22
- env.bio_10 1 444.40 488.40
- env.bio_6 1 444.44 488.44
- env.elev 1 444.77 488.77
<none> 442.88 488.88
- env.bio_5 1 445.02 489.02
- env.bio_7 1 446.71 490.71
- env.woodysavanna 1 446.99 490.99
- env.bio_11 1 447.18 491.18
- env.bio_13 1 447.24 491.24
- env.grass 1 447.89 491.89
- env.bio_19 1 448.07 492.07
- env.bio_4 1 448.35 492.35
- env.bio_3 1 455.55 499.55
- env.bio_2 1 469.28 513.28
- env.bio_12 1 470.34 514.34
- env.urban 1 472.70 516.70
- env.npp 1 477.84 521.84
Step: AIC=487.74
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_15 + env.bio_16 + env.bio_17 + env.bio_19 + env.elev +
env.urban + env.barren + env.savanna + env.woodysavanna +
env.grass + env.npp
Df Deviance AIC
- env.barren 1 444.83 486.83
- env.savanna 1 444.85 486.85
- env.bio_6 1 444.90 486.90
- env.bio_17 1 445.14 487.14
- env.bio_16 1 445.14 487.14
- env.bio_15 1 445.14 487.14
- env.bio_10 1 445.19 487.19
- env.elev 1 445.55 487.55
<none> 443.74 487.74
- env.bio_5 1 446.71 488.71
- env.bio_7 1 446.85 488.85
- env.bio_13 1 447.62 489.62
- env.woodysavanna 1 447.75 489.75
- env.bio_11 1 447.81 489.81
- env.bio_19 1 448.92 490.92
- env.grass 1 449.07 491.07
- env.bio_4 1 450.84 492.84
- env.bio_3 1 457.41 499.41
- env.bio_2 1 471.33 513.33
- env.bio_12 1 471.61 513.61
- env.urban 1 473.04 515.04
- env.npp 1 481.49 523.49
Step: AIC=486.83
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_15 + env.bio_16 + env.bio_17 + env.bio_19 + env.elev +
env.urban + env.savanna + env.woodysavanna + env.grass +
env.npp
Df Deviance AIC
- env.savanna 1 445.78 485.78
- env.bio_6 1 445.83 485.83
- env.bio_10 1 446.17 486.17
- env.bio_17 1 446.22 486.22
- env.bio_16 1 446.22 486.22
- env.bio_15 1 446.22 486.22
- env.elev 1 446.60 486.60
<none> 444.83 486.83
- env.bio_5 1 447.53 487.53
- env.bio_7 1 447.83 487.83
- env.bio_13 1 448.77 488.77
- env.bio_11 1 448.80 488.80
- env.woodysavanna 1 449.02 489.02
- env.bio_19 1 450.03 490.03
- env.grass 1 450.75 490.75
- env.bio_4 1 451.50 491.50
- env.bio_3 1 458.11 498.11
- env.bio_12 1 472.60 512.60
- env.bio_2 1 474.00 514.00
- env.urban 1 474.60 514.60
- env.npp 1 489.02 529.02
Step: AIC=485.78
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_6 +
env.bio_7 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_15 + env.bio_16 + env.bio_17 + env.bio_19 + env.elev +
env.urban + env.woodysavanna + env.grass + env.npp
Df Deviance AIC
- env.bio_6 1 446.84 484.84
- env.bio_10 1 447.26 485.26
- env.bio_17 1 447.33 485.33
- env.bio_16 1 447.33 485.33
- env.bio_15 1 447.33 485.33
- env.elev 1 447.42 485.42
<none> 445.78 485.78
- env.bio_5 1 448.34 486.34
- env.bio_7 1 448.79 486.79
- env.bio_13 1 449.45 487.45
- env.bio_11 1 449.90 487.90
- env.bio_19 1 450.51 488.51
- env.grass 1 450.81 488.81
- env.woodysavanna 1 451.28 489.28
- env.bio_4 1 453.06 491.06
- env.bio_3 1 459.33 497.33
- env.bio_12 1 472.93 510.93
- env.urban 1 475.65 513.65
- env.bio_2 1 475.96 513.96
- env.npp 1 491.07 529.07
Step: AIC=484.84
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_7 +
env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_15 +
env.bio_16 + env.bio_17 + env.bio_19 + env.elev + env.urban +
env.woodysavanna + env.grass + env.npp
Df Deviance AIC
- env.elev 1 448.06 484.06
- env.bio_10 1 448.22 484.22
- env.bio_17 1 448.24 484.24
- env.bio_16 1 448.24 484.24
- env.bio_15 1 448.24 484.24
- env.bio_5 1 448.56 484.56
<none> 446.84 484.84
- env.bio_7 1 450.88 486.88
- env.bio_11 1 451.02 487.02
- env.bio_13 1 451.31 487.31
- env.grass 1 451.76 487.76
- env.bio_19 1 451.86 487.86
- env.woodysavanna 1 452.34 488.34
- env.bio_4 1 453.54 489.54
- env.bio_3 1 459.84 495.84
- env.bio_12 1 472.99 508.99
- env.bio_2 1 476.18 512.18
- env.urban 1 478.41 514.41
- env.npp 1 491.43 527.43
Step: AIC=484.06
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_7 +
env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_15 +
env.bio_16 + env.bio_17 + env.bio_19 + env.urban + env.woodysavanna +
env.grass + env.npp
Df Deviance AIC
- env.bio_17 1 449.25 483.25
- env.bio_16 1 449.25 483.25
- env.bio_15 1 449.25 483.25
- env.bio_10 1 449.44 483.44
<none> 448.06 484.06
- env.bio_5 1 450.32 484.32
- env.bio_11 1 451.97 485.97
- env.bio_13 1 452.19 486.19
- env.grass 1 452.58 486.58
- env.bio_19 1 452.82 486.82
- env.woodysavanna 1 453.79 487.79
- env.bio_7 1 454.03 488.03
- env.bio_4 1 455.29 489.29
- env.bio_3 1 460.53 494.53
- env.bio_12 1 474.41 508.41
- env.bio_2 1 476.23 510.23
- env.urban 1 479.48 513.48
- env.npp 1 491.71 525.71
Step: AIC=483.25
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_7 +
env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_15 +
env.bio_16 + env.bio_19 + env.urban + env.woodysavanna +
env.grass + env.npp
Df Deviance AIC
- env.bio_10 1 450.49 482.49
- env.bio_5 1 451.15 483.15
<none> 449.25 483.25
- env.bio_13 1 452.60 484.60
- env.bio_11 1 452.85 484.85
- env.grass 1 453.60 485.60
- env.bio_19 1 454.02 486.02
- env.woodysavanna 1 454.50 486.50
- env.bio_7 1 454.62 486.62
- env.bio_4 1 455.62 487.62
- env.bio_16 1 458.99 490.99
- env.bio_3 1 460.81 492.81
- env.bio_12 1 474.41 506.41
- env.bio_2 1 476.72 508.72
- env.bio_15 1 478.92 510.92
- env.urban 1 481.99 513.99
- env.npp 1 492.81 524.81
Step: AIC=482.49
presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 + env.bio_7 +
env.bio_11 + env.bio_12 + env.bio_13 + env.bio_15 + env.bio_16 +
env.bio_19 + env.urban + env.woodysavanna + env.grass + env.npp
Df Deviance AIC
<none> 450.49 482.49
- env.bio_5 1 452.78 482.78
- env.bio_11 1 452.96 482.96
- env.bio_13 1 454.30 484.30
- env.grass 1 454.75 484.75
- env.bio_19 1 455.32 485.32
- env.woodysavanna 1 455.49 485.49
- env.bio_7 1 455.65 485.65
- env.bio_4 1 455.66 485.66
- env.bio_16 1 459.97 489.97
- env.bio_3 1 461.05 491.05
- env.bio_12 1 474.41 504.41
- env.urban 1 482.00 512.00
- env.bio_15 1 484.60 514.60
- env.npp 1 493.31 523.31
- env.bio_2 1 494.44 524.44
Call: glm(formula = presabs ~ env.bio_2 + env.bio_3 + env.bio_4 + env.bio_5 +
env.bio_7 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_15 +
env.bio_16 + env.bio_19 + env.urban + env.woodysavanna +
env.grass + env.npp, family = "binomial", data = presabs.glm)
Coefficients:
(Intercept) env.bio_2 env.bio_3 env.bio_4
-2.0968 9.6369 -10.2678 2.0632
env.bio_5 env.bio_7 env.bio_11 env.bio_12
-1.2283 1.0403 -0.6014 7.6178
env.bio_13 env.bio_15 env.bio_16 env.bio_19
-2.0964 -12.2221 15.3985 1.8305
env.urban env.woodysavanna env.grass env.npp
1.0692 0.5192 -0.4754 1.2654
Degrees of Freedom: 558 Total (i.e. Null); 543 Residual
Null Deviance: 650.2
Residual Deviance: 450.5 AIC: 482.5Boosted 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 = 1.1646
tolerance is fixed at 0.0012
ntrees resid. dev.
50 1.0486
now adding trees...
100 0.979
150 0.9358
200 0.9087
250 0.8922
300 0.8781
350 0.8673
400 0.8574
450 0.8488
500 0.843
550 0.8367
600 0.8315
650 0.8273
700 0.823
750 0.8185
800 0.8154
850 0.8143
900 0.8114
950 0.8096
1000 0.8086
1050 0.8076
1100 0.8049
1150 0.8038
1200 0.8026
1250 0.8016
1300 0.8004
1350 0.7981
1400 0.797
1450 0.7976
1500 0.7979
1550 0.7976
1600 0.7984
1650 0.7985
1700 0.7974
1750 0.7971
1800 0.7977
1850 0.7978
1900 0.7985
1950 0.7979
2000 0.7974
2050 0.798
2100 0.7982
2150 0.799 
mean total deviance = 1.165
mean residual deviance = 0.627
estimated cv deviance = 0.797 ; se = 0.032
training data correlation = 0.742
cv correlation = 0.615 ; se = 0.029
training data AUC score = 0.924
cv AUC score = 0.855 ; se = 0.016
elapsed time - 0.1 minutes Code
summary(brt)
var rel.inf
env.urban env.urban 17.26861395
env.npp env.npp 15.01517364
env.woodysavanna env.woodysavanna 14.16072459
env.bio_10 env.bio_10 9.23881041
env.bio_7 env.bio_7 7.42318604
env.bio_15 env.bio_15 4.92413959
env.nonveg env.nonveg 4.66377672
env.bio_13 env.bio_13 4.19897610
env.bio_9 env.bio_9 3.27987940
env.bio_19 env.bio_19 3.23337226
env.bio_8 env.bio_8 3.10326169
env.bio_4 env.bio_4 2.60126262
env.grass env.grass 2.12961370
env.bio_5 env.bio_5 1.67426061
env.bio_16 env.bio_16 1.38605132
env.bio_11 env.bio_11 1.08115667
env.tree env.tree 1.04394678
env.bio_3 env.bio_3 1.04314388
env.bio_12 env.bio_12 0.63657303
env.bio_14 env.bio_14 0.35043006
env.savanna env.savanna 0.32227466
env.bio_6 env.bio_6 0.30825035
env.bio_17 env.bio_17 0.30550370
env.wetland env.wetland 0.13947752
env.bio_1 env.bio_1 0.12088078
env.elev env.elev 0.11976521
env.bio_2 env.bio_2 0.10446486
env.bio_18 env.bio_18 0.09746777
env.nontree env.nontree 0.02556210
env.barren env.barren 0.00000000
env.water env.water 0.00000000Code
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
3.8830559 6.1994138 4.5932245 7.3239922
env.bio_5 env.bio_6 env.bio_7 env.bio_8
7.2002733 5.2138027 8.9940396 5.5199000
env.bio_9 env.bio_10 env.bio_11 env.bio_12
6.1900716 11.3700848 5.3196636 5.6688179
env.bio_13 env.bio_14 env.bio_15 env.bio_16
5.5827974 5.8312823 8.2539183 4.7307267
env.bio_17 env.bio_18 env.bio_19 env.elev
6.2734100 4.9970425 5.0778873 7.3248256
env.urban env.barren env.water env.savanna
21.0199917 0.2081271 0.9086772 3.6009138
env.woodysavanna env.wetland env.grass env.npp
15.5240477 1.7918203 3.3481191 17.6537944
env.tree env.nontree env.nonveg
5.2666224 4.7026054 8.3579938 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.urban, env.npp, env.woodysavanna, env.bio_10, env.bio_7, env.bio_15
- Random forest: env.urban, env.woodysavanna, env.npp, env.bio_10, env.bio_7, env.nonveg
- Ranger: env.urban, env.npp, env.woodysavanna, env.bio_10, env.bio_7, env.nonveg
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.urban | env.npp | env.woodysavanna | env.bio_10 | env.bio_7 | env.bio_15 | env.nonveg | |
|---|---|---|---|---|---|---|---|
| env.urban | 1.0000000 | 0.1344147 | 0.0318901 | 0.2582501 | 0.0225795 | -0.2740025 | -0.0958183 |
| env.npp | 0.1344147 | 1.0000000 | 0.0006886 | 0.2583383 | -0.2260104 | -0.6102193 | -0.4653541 |
| env.woodysavanna | 0.0318901 | 0.0006886 | 1.0000000 | 0.0182359 | 0.1850820 | 0.0462821 | -0.1058182 |
| env.bio_10 | 0.2582501 | 0.2583383 | 0.0182359 | 1.0000000 | -0.1345391 | -0.4429341 | -0.1873301 |
| env.bio_7 | 0.0225795 | -0.2260104 | 0.1850820 | -0.1345391 | 1.0000000 | 0.2496259 | -0.1488980 |
| env.bio_15 | -0.2740025 | -0.6102193 | 0.0462821 | -0.4429341 | 0.2496259 | 1.0000000 | 0.1918470 |
| env.nonveg | -0.0958183 | -0.4653541 | -0.1058182 | -0.1873301 | -0.1488980 | 0.1918470 | 1.0000000 |
Code
tmpFiles(remove=T)