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)Cerdocyon thous Variable Selection
Variable Selection for Cerdocyon thous, 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()Cerdocyon thous’ preferences
We will use the data from both periods
Code
PA_time1 <- readRDS('data/species_POPA_data/PA_cthous_time1_blobs.rds')%>% ungroup()
PA_time2 <- readRDS('data/species_POPA_data/PA_cthous_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 | 0 | 1769 days | 2404 | 537081878 [m^2] |
| 3 | 0 | 6222 days | 11078 | 2241827859 [m^2] |
| 4 | 0 | 720 days | 1291 | 162791738 [m^2] |
| 5 | 0 | 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 | 0 | 510 days | 11095 | 2.229446e+09 [m^2] |
| 2 | 0 | 150 days | 12276 | 1.542295e+03 [m^2] |
| 3 | 0 | 407 days | 1585 | 1.049520e-01 [m^2] |
| 4 | 0 | 869 days | 1440 | 7.494574e+01 [m^2] |
| 5 | 0 | 70 days | 400 | 2.546836e+01 [m^2] |
| 6 | 0 | 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
-2.8531 -0.7432 -0.1290 0.8810 2.3182
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.891e+01 1.219e+02 -0.401 0.68824
env.bio_1 -1.144e+01 7.343e+00 -1.558 0.11912
env.bio_2 -3.418e+00 6.546e+00 -0.522 0.60161
env.bio_3 3.325e+01 1.359e+01 2.447 0.01442 *
env.bio_4 -4.941e+00 2.300e+00 -2.148 0.03168 *
env.bio_5 1.980e-01 1.505e+00 0.132 0.89532
env.bio_6 -2.994e+00 1.778e+00 -1.684 0.09210 .
env.bio_7 -2.988e+00 7.645e-01 -3.909 9.28e-05 ***
env.bio_8 3.632e+00 2.303e+00 1.577 0.11476
env.bio_9 6.168e-01 2.199e+00 0.280 0.77914
env.bio_10 2.486e-01 4.234e-01 0.587 0.55702
env.bio_11 1.637e+00 5.830e-01 2.808 0.00498 **
env.bio_12 1.938e+00 1.807e+00 1.072 0.28351
env.bio_13 -2.599e+00 1.244e+00 -2.090 0.03664 *
env.bio_14 1.377e+01 7.090e+00 1.943 0.05205 .
env.bio_15 2.382e+04 8.986e+05 0.027 0.97885
env.bio_16 -3.994e+04 1.506e+06 -0.027 0.97885
env.bio_17 -3.394e+04 1.280e+06 -0.027 0.97885
env.bio_18 -7.905e-01 8.638e-01 -0.915 0.36009
env.bio_19 -5.695e-01 1.034e+00 -0.551 0.58169
env.elev 4.861e-01 7.869e-01 0.618 0.53676
env.urban 1.221e+00 3.026e-01 4.036 5.45e-05 ***
env.barren -2.411e+02 6.152e+02 -0.392 0.69511
env.water -2.108e-01 4.440e-01 -0.475 0.63489
env.savanna -3.835e-01 2.229e-01 -1.721 0.08528 .
env.woodysavanna 2.633e-01 2.205e-01 1.194 0.23252
env.wetland -4.071e-01 1.948e-01 -2.090 0.03659 *
env.grass -4.602e-01 2.964e-01 -1.552 0.12057
env.npp 6.727e-01 2.610e-01 2.578 0.00994 **
env.tree -5.638e-01 4.962e-01 -1.136 0.25593
env.nontree 5.172e-01 4.647e-01 1.113 0.26576
env.nonveg 1.916e-01 5.500e-01 0.348 0.72750
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 759.39 on 558 degrees of freedom
Residual deviance: 521.20 on 527 degrees of freedom
AIC: 585.2
Number of Fisher Scoring iterations: 14Code
step(glm.full) # step might not work with gam so glmStart: AIC=585.2
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.bio_15 1 521.20 583.20
- env.bio_17 1 521.20 583.20
- env.bio_16 1 521.20 583.20
- env.bio_5 1 521.21 583.21
- env.bio_9 1 521.28 583.28
- env.nonveg 1 521.32 583.32
- env.water 1 521.43 583.43
- env.bio_2 1 521.47 583.47
- env.bio_19 1 521.51 583.51
- env.bio_10 1 521.54 583.54
- env.elev 1 521.58 583.58
- env.bio_18 1 522.05 584.05
- env.bio_12 1 522.35 584.35
- env.nontree 1 522.44 584.44
- env.tree 1 522.48 584.48
- env.barren 1 522.48 584.48
- env.woodysavanna 1 522.64 584.64
<none> 521.20 585.20
- env.grass 1 523.65 585.65
- env.bio_1 1 523.67 585.67
- env.bio_8 1 523.74 585.74
- env.bio_6 1 524.15 586.15
- env.savanna 1 524.26 586.26
- env.bio_14 1 525.11 587.11
- env.bio_13 1 525.82 587.82
- env.bio_4 1 526.04 588.04
- env.bio_3 1 527.49 589.49
- env.npp 1 528.12 590.12
- env.wetland 1 528.58 590.58
- env.bio_11 1 530.41 592.41
- env.bio_7 1 538.19 600.19
- env.urban 1 541.70 603.70
Step: AIC=583.2
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_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.bio_5 1 521.22 581.22
- env.bio_9 1 521.28 581.28
- env.nonveg 1 521.32 581.32
- env.water 1 521.43 581.43
- env.bio_2 1 521.47 581.47
- env.bio_19 1 521.51 581.51
- env.bio_10 1 521.54 581.54
- env.elev 1 521.58 581.58
- env.bio_18 1 522.05 582.05
- env.bio_12 1 522.37 582.37
- env.nontree 1 522.44 582.44
- env.barren 1 522.49 582.49
- env.tree 1 522.49 582.49
- env.woodysavanna 1 522.66 582.66
<none> 521.20 583.20
- env.grass 1 523.65 583.65
- env.bio_1 1 523.67 583.67
- env.bio_8 1 523.76 583.76
- env.bio_6 1 524.15 584.15
- env.savanna 1 524.26 584.26
- env.bio_17 1 524.44 584.44
- env.bio_14 1 525.13 585.13
- env.bio_13 1 525.95 585.95
- env.bio_4 1 526.13 586.13
- env.bio_16 1 526.17 586.17
- env.bio_3 1 527.53 587.53
- env.npp 1 528.12 588.12
- env.wetland 1 528.59 588.59
- env.bio_11 1 530.41 590.41
- env.bio_7 1 538.43 598.43
- env.urban 1 541.77 601.77
Step: AIC=581.22
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + 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_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.bio_9 1 521.29 579.29
- env.nonveg 1 521.33 579.33
- env.water 1 521.46 579.46
- env.bio_2 1 521.50 579.50
- env.bio_10 1 521.55 579.55
- env.bio_19 1 521.55 579.55
- env.elev 1 521.58 579.58
- env.bio_18 1 522.09 580.09
- env.barren 1 522.50 580.50
- env.tree 1 522.50 580.50
- env.nontree 1 522.53 580.53
- env.bio_12 1 522.55 580.55
- env.woodysavanna 1 522.66 580.66
<none> 521.22 581.22
- env.grass 1 523.71 581.71
- env.bio_1 1 523.72 581.72
- env.bio_6 1 524.18 582.18
- env.savanna 1 524.29 582.29
- env.bio_17 1 524.72 582.72
- env.bio_14 1 525.14 583.14
- env.bio_8 1 526.10 584.10
- env.bio_13 1 526.12 584.12
- env.bio_4 1 526.13 584.13
- env.bio_16 1 526.24 584.24
- env.bio_3 1 527.55 585.55
- env.npp 1 528.13 586.13
- env.wetland 1 528.60 586.60
- env.bio_11 1 530.56 588.56
- env.bio_7 1 538.93 596.93
- env.urban 1 541.77 599.77
Step: AIC=579.29
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_6 +
env.bio_7 + env.bio_8 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + 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 1 521.41 577.41
- env.water 1 521.53 577.53
- env.bio_2 1 521.53 577.53
- env.bio_19 1 521.61 577.61
- env.bio_10 1 521.63 577.63
- env.elev 1 521.69 577.69
- env.bio_18 1 522.21 578.21
- env.nontree 1 522.59 578.59
- env.barren 1 522.60 578.60
- env.tree 1 522.63 578.63
- env.woodysavanna 1 522.74 578.74
- env.bio_12 1 522.89 578.89
<none> 521.29 579.29
- env.grass 1 523.96 579.96
- env.bio_1 1 524.12 580.12
- env.savanna 1 524.57 580.57
- env.bio_14 1 525.26 581.26
- env.bio_17 1 525.51 581.51
- env.bio_13 1 526.28 582.28
- env.bio_8 1 526.34 582.34
- env.bio_4 1 526.57 582.57
- env.bio_16 1 526.62 582.62
- env.bio_3 1 527.73 583.73
- env.wetland 1 528.62 584.62
- env.npp 1 528.81 584.81
- env.bio_11 1 531.43 587.43
- env.bio_6 1 532.97 588.97
- env.bio_7 1 539.43 595.43
- env.urban 1 541.77 597.77
Step: AIC=577.41
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_6 +
env.bio_7 + env.bio_8 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + 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 1 521.60 575.60
- env.bio_2 1 521.63 575.63
- env.elev 1 521.77 575.77
- env.bio_10 1 521.78 575.78
- env.bio_19 1 521.79 575.79
- env.bio_18 1 522.37 576.37
- env.barren 1 522.62 576.62
- env.tree 1 522.86 576.86
- env.bio_12 1 522.90 576.90
- env.woodysavanna 1 522.96 576.96
<none> 521.41 577.41
- env.nontree 1 523.97 577.97
- env.grass 1 523.99 577.99
- env.bio_1 1 524.15 578.15
- env.savanna 1 524.57 578.57
- env.bio_14 1 525.26 579.26
- env.bio_17 1 525.53 579.53
- env.bio_13 1 526.29 580.29
- env.bio_8 1 526.48 580.48
- env.bio_4 1 526.68 580.68
- env.bio_16 1 526.68 580.68
- env.bio_3 1 527.73 581.73
- env.wetland 1 528.66 582.66
- env.npp 1 529.18 583.18
- env.bio_11 1 531.76 585.76
- env.bio_6 1 533.23 587.23
- env.bio_7 1 539.52 593.52
- env.urban 1 541.77 595.77
Step: AIC=575.6
presabs ~ env.bio_1 + env.bio_2 + env.bio_3 + env.bio_4 + env.bio_6 +
env.bio_7 + env.bio_8 + env.bio_10 + env.bio_11 + env.bio_12 +
env.bio_13 + env.bio_14 + 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 1 521.82 573.82
- env.elev 1 521.93 573.93
- env.bio_10 1 521.95 573.95
- env.bio_19 1 521.97 573.97
- env.bio_18 1 522.54 574.54
- env.barren 1 522.87 574.87
- env.bio_12 1 523.18 575.18
- env.woodysavanna 1 523.20 575.20
<none> 521.60 575.60
- env.grass 1 524.11 576.11
- env.bio_1 1 524.33 576.33
- env.savanna 1 524.69 576.69
- env.nontree 1 524.72 576.72
- env.tree 1 525.26 577.26
- env.bio_14 1 525.51 577.51
- env.bio_17 1 525.83 577.83
- env.bio_13 1 526.51 578.51
- env.bio_8 1 526.65 578.65
- env.bio_4 1 526.86 578.86
- env.bio_16 1 526.92 578.92
- env.bio_3 1 527.96 579.96
- env.wetland 1 528.66 580.66
- env.npp 1 530.15 582.15
- env.bio_11 1 531.84 583.84
- env.bio_6 1 533.35 585.35
- env.bio_7 1 539.71 591.71
- env.urban 1 542.21 594.21
Step: AIC=573.82
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_10 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + 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_10 1 522.09 572.09
- env.bio_19 1 522.14 572.14
- env.elev 1 522.25 572.25
- env.bio_18 1 522.72 572.72
- env.barren 1 523.13 573.13
- env.woodysavanna 1 523.30 573.30
- env.bio_12 1 523.81 573.81
<none> 521.82 573.82
- env.grass 1 524.40 574.40
- env.bio_1 1 524.88 574.88
- env.savanna 1 524.95 574.95
- env.nontree 1 524.98 574.98
- env.tree 1 526.08 576.08
- env.bio_8 1 526.86 576.86
- env.bio_4 1 526.91 576.91
- env.bio_13 1 527.33 577.33
- env.bio_14 1 527.50 577.50
- env.bio_17 1 528.16 578.16
- env.bio_16 1 528.49 578.49
- env.wetland 1 528.79 578.79
- env.bio_3 1 529.03 579.03
- env.npp 1 530.20 580.20
- env.bio_11 1 531.85 581.85
- env.bio_6 1 534.22 584.22
- env.bio_7 1 540.47 590.47
- env.urban 1 542.56 592.56
Step: AIC=572.09
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
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_19 1 522.41 570.41
- env.elev 1 522.52 570.52
- env.bio_18 1 522.87 570.87
- env.barren 1 523.44 571.44
- env.woodysavanna 1 523.67 571.67
<none> 522.09 572.09
- env.bio_12 1 524.59 572.59
- env.grass 1 524.62 572.62
- env.bio_1 1 524.94 572.94
- env.savanna 1 525.15 573.15
- env.nontree 1 525.15 573.15
- env.tree 1 526.39 574.39
- env.bio_4 1 526.95 574.95
- env.bio_8 1 527.58 575.58
- env.bio_13 1 527.77 575.77
- env.bio_14 1 528.33 576.33
- env.bio_3 1 529.21 577.21
- env.wetland 1 529.24 577.24
- env.bio_16 1 530.19 578.19
- env.npp 1 530.57 578.57
- env.bio_17 1 532.08 580.08
- env.bio_11 1 533.88 581.88
- env.bio_6 1 534.26 582.26
- env.bio_7 1 540.52 588.52
- env.urban 1 544.97 592.97
Step: AIC=570.41
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_16 + env.bio_17 + env.bio_18 + 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 1 522.86 568.86
- env.bio_18 1 522.89 568.89
- env.barren 1 523.86 569.86
- env.woodysavanna 1 523.99 569.99
<none> 522.41 570.41
- env.bio_12 1 524.75 570.75
- env.grass 1 524.86 570.86
- env.nontree 1 525.39 571.39
- env.savanna 1 525.58 571.58
- env.bio_1 1 526.32 572.32
- env.tree 1 526.49 572.49
- env.bio_4 1 527.45 573.45
- env.bio_13 1 527.78 573.78
- env.bio_8 1 528.02 574.02
- env.wetland 1 529.35 575.35
- env.bio_14 1 529.66 575.66
- env.bio_3 1 530.82 576.82
- env.npp 1 531.40 577.40
- env.bio_17 1 533.01 579.01
- env.bio_16 1 534.03 580.03
- env.bio_11 1 534.26 580.26
- env.bio_6 1 535.52 581.52
- env.bio_7 1 542.02 588.02
- env.urban 1 545.54 591.54
Step: AIC=568.86
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_16 + env.bio_17 + env.bio_18 + env.urban + env.barren +
env.savanna + env.woodysavanna + env.wetland + env.grass +
env.npp + env.tree + env.nontree
Df Deviance AIC
- env.bio_18 1 523.21 567.21
- env.woodysavanna 1 524.42 568.42
- env.barren 1 524.44 568.44
<none> 522.86 568.86
- env.bio_12 1 525.07 569.07
- env.grass 1 525.33 569.33
- env.savanna 1 525.93 569.93
- env.nontree 1 525.93 569.93
- env.bio_1 1 526.47 570.47
- env.tree 1 527.11 571.11
- env.bio_4 1 527.90 571.90
- env.bio_13 1 528.02 572.02
- env.bio_8 1 528.18 572.18
- env.wetland 1 529.60 573.60
- env.bio_14 1 529.67 573.67
- env.bio_3 1 530.87 574.87
- env.npp 1 531.67 575.67
- env.bio_17 1 533.42 577.42
- env.bio_11 1 535.34 579.34
- env.bio_16 1 535.42 579.42
- env.bio_6 1 535.54 579.54
- env.bio_7 1 542.08 586.08
- env.urban 1 546.15 590.15
Step: AIC=567.21
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_16 + env.bio_17 + env.urban + env.barren + env.savanna +
env.woodysavanna + env.wetland + env.grass + env.npp + env.tree +
env.nontree
Df Deviance AIC
- env.woodysavanna 1 524.68 566.68
- env.barren 1 524.81 566.81
<none> 523.21 567.21
- env.bio_12 1 525.45 567.45
- env.grass 1 525.96 567.96
- env.savanna 1 526.35 568.35
- env.bio_1 1 526.72 568.72
- env.nontree 1 526.82 568.82
- env.bio_4 1 528.04 570.04
- env.tree 1 528.10 570.10
- env.bio_8 1 528.29 570.29
- env.bio_13 1 528.86 570.86
- env.bio_14 1 529.68 571.68
- env.wetland 1 530.34 572.34
- env.bio_3 1 530.87 572.87
- env.npp 1 531.79 573.79
- env.bio_17 1 533.43 575.43
- env.bio_16 1 535.71 577.71
- env.bio_6 1 535.79 577.79
- env.bio_11 1 536.53 578.53
- env.bio_7 1 542.15 584.15
- env.urban 1 546.25 588.25
Step: AIC=566.68
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_16 + env.bio_17 + env.urban + env.barren + env.savanna +
env.wetland + env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
- env.barren 1 526.66 566.66
<none> 524.68 566.68
- env.bio_12 1 527.02 567.02
- env.bio_1 1 528.20 568.20
- env.nontree 1 528.71 568.71
- env.grass 1 529.64 569.64
- env.bio_4 1 530.65 570.65
- env.bio_8 1 530.70 570.70
- env.bio_13 1 530.87 570.87
- env.bio_14 1 530.91 570.91
- env.savanna 1 530.95 570.95
- env.tree 1 531.65 571.65
- env.bio_3 1 532.22 572.22
- env.npp 1 532.43 572.43
- env.wetland 1 533.81 573.81
- env.bio_17 1 534.72 574.72
- env.bio_6 1 536.29 576.29
- env.bio_16 1 536.73 576.73
- env.bio_11 1 537.95 577.95
- env.bio_7 1 543.63 583.63
- env.urban 1 547.30 587.30
Step: AIC=566.66
presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 + env.bio_7 +
env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 + env.bio_14 +
env.bio_16 + env.bio_17 + env.urban + env.savanna + env.wetland +
env.grass + env.npp + env.tree + env.nontree
Df Deviance AIC
<none> 526.66 566.66
- env.bio_12 1 528.73 566.73
- env.bio_1 1 529.73 567.73
- env.grass 1 531.26 569.26
- env.nontree 1 532.11 570.11
- env.savanna 1 532.18 570.18
- env.bio_14 1 532.63 570.63
- env.bio_4 1 532.63 570.63
- env.bio_8 1 532.74 570.74
- env.bio_13 1 532.78 570.78
- env.tree 1 533.11 571.11
- env.bio_3 1 534.08 572.08
- env.wetland 1 535.70 573.70
- env.bio_17 1 536.28 574.28
- env.bio_6 1 537.96 575.96
- env.npp 1 538.23 576.23
- env.bio_11 1 540.01 578.01
- env.bio_16 1 540.47 578.47
- env.bio_7 1 545.31 583.31
- env.urban 1 550.50 588.50
Call: glm(formula = presabs ~ env.bio_1 + env.bio_3 + env.bio_4 + env.bio_6 +
env.bio_7 + env.bio_8 + env.bio_11 + env.bio_12 + env.bio_13 +
env.bio_14 + env.bio_16 + env.bio_17 + env.urban + env.savanna +
env.wetland + env.grass + env.npp + env.tree + env.nontree,
family = "binomial", data = presabs.glm)
Coefficients:
(Intercept) env.bio_1 env.bio_3 env.bio_4 env.bio_6 env.bio_7
-1.2128 -10.8305 27.4563 -4.7145 -2.2691 -2.9086
env.bio_8 env.bio_11 env.bio_12 env.bio_13 env.bio_14 env.bio_16
3.8727 1.5414 2.1612 -2.7013 10.0441 -13.1920
env.bio_17 env.urban env.savanna env.wetland env.grass env.npp
-8.6567 1.1790 -0.4522 -0.4013 -0.5579 0.7006
env.tree env.nontree
-0.6656 0.6079
Degrees of Freedom: 558 Total (i.e. Null); 539 Residual
Null Deviance: 759.4
Residual Deviance: 526.7 AIC: 566.7Boosted 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 = 1.3602
tolerance is fixed at 0.0014
ntrees resid. dev.
50 1.2522
now adding trees...
100 1.1864
150 1.1448
200 1.1214
250 1.1071
300 1.0936
350 1.081
400 1.0705
450 1.0627
500 1.0556
550 1.0495
600 1.0452
650 1.0395
700 1.0349
750 1.0317
800 1.0282
850 1.0259
900 1.0229
950 1.0211
1000 1.018
1050 1.0162
1100 1.0148
1150 1.0133
1200 1.0121
1250 1.0091
1300 1.0073
1350 1.0063
1400 1.0051
1450 1.0038
1500 1.003
1550 1.0012
1600 1.0001
1650 0.9986
1700 0.9974
1750 0.9964
1800 0.9955
1850 0.9948
1900 0.9929
1950 0.9934
2000 0.9929
2050 0.9926
2100 0.9913
2150 0.9898
2200 0.9902
2250 0.9891
2300 0.9883
2350 0.9883
2400 0.9876
2450 0.9871
2500 0.9866
2550 0.9857
2600 0.9867
2650 0.9863
2700 0.9858
2750 0.9857
2800 0.9858
2850 0.986
2900 0.9856
2950 0.9851
3000 0.9861
3050 0.9873
3100 0.9873
3150 0.9863 
mean total deviance = 1.36
mean residual deviance = 0.703
estimated cv deviance = 0.985 ; se = 0.028
training data correlation = 0.761
cv correlation = 0.574 ; se = 0.016
training data AUC score = 0.938
cv AUC score = 0.83 ; se = 0.01
elapsed time - 0.16 minutes Code
summary(brt)
var rel.inf
env.tree env.tree 10.0299644
env.urban env.urban 7.5111653
env.npp env.npp 7.4734145
env.bio_19 env.bio_19 7.3340558
env.bio_15 env.bio_15 6.5204476
env.bio_17 env.bio_17 5.7097991
env.bio_7 env.bio_7 5.4923361
env.bio_13 env.bio_13 5.3982862
env.bio_16 env.bio_16 5.3331750
env.grass env.grass 5.1821453
env.bio_10 env.bio_10 3.8833683
env.bio_3 env.bio_3 3.2658389
env.bio_1 env.bio_1 3.0814506
env.elev env.elev 2.7462432
env.bio_6 env.bio_6 2.6768487
env.savanna env.savanna 2.5404933
env.bio_5 env.bio_5 2.3301520
env.woodysavanna env.woodysavanna 2.0849750
env.bio_14 env.bio_14 1.9429295
env.nonveg env.nonveg 1.6855494
env.bio_18 env.bio_18 1.6322938
env.bio_4 env.bio_4 1.4612438
env.bio_12 env.bio_12 1.4372397
env.bio_9 env.bio_9 0.8189502
env.bio_8 env.bio_8 0.7404866
env.wetland env.wetland 0.6623812
env.bio_11 env.bio_11 0.4641735
env.bio_2 env.bio_2 0.3871408
env.nontree env.nontree 0.1734525
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
11.4866155 6.4421674 15.6746082 10.6558984
env.bio_5 env.bio_6 env.bio_7 env.bio_8
7.8839045 5.9419526 7.4317895 8.4013731
env.bio_9 env.bio_10 env.bio_11 env.bio_12
6.0391354 11.1125221 5.8173381 7.1467652
env.bio_13 env.bio_14 env.bio_15 env.bio_16
9.5942731 10.4530989 10.3222644 13.9337983
env.bio_17 env.bio_18 env.bio_19 env.elev
14.8842593 7.4406250 18.3120989 8.6122978
env.urban env.barren env.water env.savanna
10.0885256 0.2394952 0.5178292 6.2253823
env.woodysavanna env.wetland env.grass env.npp
5.2934251 1.7176239 5.0663925 6.2539165
env.tree env.nontree env.nonveg
12.5672480 7.2960651 6.4150878 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.urban, env.npp, env.bio_19, env.bio_15, env.bio_17
- Random forest: env.bio_19, env.bio_16, env.bio_3, env.tree, env.bio_17, env.bio_10
- Ranger: env.bio_19, env.bio_3, env.bio_17, env.bio_16, env.tree, env.bio_1
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.urban | env.npp | env.bio_19 | env.bio_15 | env.bio_17 | env.bio_16 | env.bio_3 | env.bio_10 | env.bio_1 | |
|---|---|---|---|---|---|---|---|---|---|---|
| env.tree | 1.0000000 | -0.0366332 | -0.0468240 | 0.2748842 | 0.0503882 | -0.4936629 | 0.4202314 | 0.2510269 | -0.0288362 | 0.2241057 |
| env.urban | -0.0366332 | 1.0000000 | 0.1344147 | -0.2207150 | -0.2740025 | -0.0078816 | -0.1491519 | -0.1778445 | 0.2582501 | -0.2201133 |
| env.npp | -0.0468240 | 0.1344147 | 1.0000000 | -0.4089540 | -0.6102193 | -0.0198950 | -0.3303117 | -0.4287304 | 0.2583383 | -0.5423448 |
| env.bio_19 | 0.2748842 | -0.2207150 | -0.4089540 | 1.0000000 | 0.7072778 | -0.6692034 | 0.9320639 | 0.9351986 | -0.3385074 | 0.9064849 |
| env.bio_15 | 0.0503882 | -0.2740025 | -0.6102193 | 0.7072778 | 1.0000000 | -0.0543381 | 0.6102757 | 0.7284579 | -0.4429341 | 0.8361199 |
| env.bio_17 | -0.4936629 | -0.0078816 | -0.0198950 | -0.6692034 | -0.0543381 | 1.0000000 | -0.8241800 | -0.6805953 | -0.0648619 | -0.5533539 |
| env.bio_16 | 0.4202314 | -0.1491519 | -0.3303117 | 0.9320639 | 0.6102757 | -0.8241800 | 1.0000000 | 0.9531144 | -0.1997582 | 0.9132283 |
| env.bio_3 | 0.2510269 | -0.1778445 | -0.4287304 | 0.9351986 | 0.7284579 | -0.6805953 | 0.9531144 | 1.0000000 | -0.2194668 | 0.9683961 |
| env.bio_10 | -0.0288362 | 0.2582501 | 0.2583383 | -0.3385074 | -0.4429341 | -0.0648619 | -0.1997582 | -0.2194668 | 1.0000000 | -0.2171595 |
| env.bio_1 | 0.2241057 | -0.2201133 | -0.5423448 | 0.9064849 | 0.8361199 | -0.5533539 | 0.9132283 | 0.9683961 | -0.2171595 | 1.0000000 |
Code
tmpFiles(remove=T)