library(data.table)
library(readxl)
library(dplyr)
library(BiodiversityR) # to get the treegoer.score() functionThe Tree Globally Observed Environmental Ranges (TreeGOER) database provides information for most known tree species of their environmental ranges for 38 bioclimatic, eight soil and three topographic variables.
In this document, I show how outputs generated by the GlobalUsefulNativeTrees (GlobUNT) online database can be filtered to match bioclimatic conditions of the planting site. Bioclimatic conditions in historical (baseline) and future climates can be sourced from the ClimateForecasts database for 15,504 weather station locations (their distribution is shown in the Zenodo archive or can be inferred via Meteostat).
ClimateForecasts was integrated in version 2024.03 of GlobUNT to filter species for 10 pre-selected variables (nine of these correspond to the variables used in case studies of the Tree Globally Observed Environmental Ranges (TreeGOER), so you could check the justification there).One of the objectives of this document was to provide some details how calculations are done internally.
(In a previous document, I showed how similar analyses could be done with custom functions and bioclimatic conditions obtained from the CitiesGOER database for 52,602 cities.)
The algorithm calculates a Climate Score for each species whereby:
This algorithm is essentially the same as the BIOCLIM model (see Booth et al. 2014 and Booth 2018).
# treegoer.file <- choose.files()
# Provide the location where the TreeGOER file
# (https://zenodo.org/records/10008994: TreeGOER_2023.txt) was downloaded to
treegoer <- fread(treegoer.file, sep="|", encoding="UTF-8")
nrow(treegoer)## [1] 2450209length(unique(treegoer$species))## [1] 48129We will calculate climate scores for the baseline climate and for median climates projected for the 2050s for Shared Socio-economic Pathway 3-7.0. Planting conditions will be inferred from the ClimateForecasts database for Nairobi.
From the ClimateForecasts, GlobUNT or from the Meteostat website, we identify the weather station location of Nairobi / Dagoretti situated near ‘The Great Corner’ in Nairobi.
# Provide the locations where the CitiesGOER files
# (https://zenodo.org/records/10004594: CitiesGOER_baseline.xlsx and CitiesGOER_2050s_ssp370.xlsx)
# were downloaded to
site.base <- data.frame(read_excel(baseline.file,
sheet="baseline data",
skip=5))
nrow(site.base) # 15504## [1] 15504head(site.base)## SEQ ID Country Subdivision_filter Station Meteostat
## 1 1 40914 Afghanistan BGL (Baghlan) Baghlan-Farm station/40914
## 2 2 69728 Afghanistan PAR (Parwan) Bagram / ?asan Kh?l station/69728
## 3 3 40945 Afghanistan BAM (Bamyan) Bamiyan station/40945
## 4 4 40967 Afghanistan WAR (-) Behsood / Sar-e Sar?b station/40967
## 5 5 40988 Afghanistan HEL (Helmand) Bust station/40988
## 6 6 40942 Afghanistan ORU (-) Chakhcharan station/40942
## LON LAT Elevation Bioclimatic bio01 bio02 bio03 bio04 bio05 bio06
## 1 68.6500 36.1000 0 TRUE 17.63 13.55 32.03 1047.75 39.8 -2.5
## 2 69.2700 34.9500 1492 TRUE 15.05 13.82 34.56 974.51 34.9 -5.1
## 3 67.8167 34.8167 0 TRUE 6.72 14.35 36.33 910.77 26.4 -13.1
## 4 67.8500 34.3000 2975 TRUE 1.95 12.98 31.49 1006.61 22.2 -19.0
## 5 64.3667 31.5500 0 TRUE 19.77 16.70 40.05 958.73 41.6 -0.1
## 6 68.4167 33.5333 0 TRUE 7.43 14.51 33.66 1019.83 28.2 -14.9
## bio07 bio08 bio09 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18
## 1 42.3 11.55 28.85 30.53 4.37 276 66 0 97.07 168 1 2
## 2 40.0 9.23 26.70 26.70 2.35 384 90 3 99.69 244 10 10
## 3 39.5 7.22 16.32 17.55 -5.13 177 38 0 84.89 100 4 6
## 4 41.2 -4.43 13.82 13.82 -11.28 354 85 0 91.60 211 6 6
## 5 41.7 10.20 31.43 31.43 7.75 110 26 0 103.57 75 1 1
## 6 43.1 1.83 17.93 19.30 -6.28 202 39 0 83.58 109 2 3
## bio19 annualPET aridityIndexThornthwaite climaticMoistureIndex continentality
## 1 108 1464.85 86.16 -0.81 29.3
## 2 145 1356.82 85.13 -0.72 27.1
## 3 48 1058.66 83.28 -0.83 25.6
## 4 124 851.58 88.35 -0.58 28.4
## 5 63 1749.44 93.71 -0.94 26.6
## 6 86 1112.68 87.20 -0.82 29.4
## embergerQ growingDegDays0 growingDegDays5 maxTempColdest meanTempColdest
## 1 22.36 6460.7 4713.2 7.5 2.5
## 2 33.32 5525.4 3936.2 6.2 0.6
## 3 16.01 2939.9 1790.7 -0.9 -7.0
## 4 31.27 1977.5 1014.0 -8.3 -13.6
## 5 8.97 7244.8 5419.8 12.5 6.2
## 6 16.75 3306.5 2096.3 -3.0 -9.0
## meanTempWarmest minTempWarmest monthCountByTemp10 PETColdestQuarter
## 1 31.8 23.8 9 37.40
## 2 27.7 20.5 7 37.62
## 3 18.6 10.7 5 23.83
## 4 14.8 7.3 3 11.55
## 5 32.8 23.9 9 55.96
## 6 20.4 12.6 5 22.70
## PETDriestQuarter PETseasonality PETWarmestQuarter PETWettestQuarter
## 1 193.42 7428.61 217.68 84.27
## 2 191.90 6310.87 191.90 80.99
## 3 146.01 5499.94 158.58 91.34
## 4 138.09 5153.11 138.09 39.09
## 5 237.84 7580.95 237.84 73.10
## 6 152.02 5828.85 166.27 61.33
## thermicityIndex MCWD elev bdod cec clay nitrogen phh2o sand silt
## 1 378.83 -1195.64 605 146.0 196.9 252.9 141.2 77.9 302.6 444.7
## 2 268.25 -1024.42 1477 NA NA NA NA NA NA NA
## 3 11.25 -881.66 2521 137.8 204.4 273.1 136.7 76.3 391.9 335.2
## 4 -123.50 -629.46 3162 137.2 256.4 311.1 207.1 77.0 315.8 372.9
## 5 420.92 -1639.46 771 147.6 147.5 264.9 65.1 80.5 402.2 332.6
## 6 50.33 -933.63 2170 149.5 177.2 344.3 97.3 78.2 271.3 384.3
## soc topoWet tri
## 1 65.1 11.32 41.38
## 2 NA 11.11 1.75
## 3 147.5 10.03 28.50
## 4 100.6 10.01 26.12
## 5 31.4 14.65 3.12
## 6 30.6 11.71 5.62site.fut <- data.frame(read_excel(future.file,
sheet="data 2050s SSP 3-7.0",
skip=5))
nrow(site.fut) # 15504## [1] 15504GlobUNT was filtered for Kenya and trees used as human food. The search resulted in 461 species.
# globunt.file <- choose.files()
globunt.file <- "C:\\Data\\Kenya_GlobUNT_HF_2023-11-15.txt"
globunt <- fread(globunt.file, sep="|", encoding="UTF-8")
nrow(globunt)## [1] 461Similar to the case studies of the TreeGOER manuscript (also see the Supplementary Script), a new data.frame is created that documents the ranges for a selected subset of environmental variables.
This database can be created via the BiodiversityR::treegoer.widen() function.
Any environmental variable from TreeGOER can be included. Here only variables included in the climate filter of GlobUNT will be included.
focal.vars <- c("bio01", "bio12",
"climaticMoistureIndex", "monthCountByTemp10", "growingDegDays5",
"bio05", "bio06", "bio16", "bio17",
"MCWD")treegoer.wide <- treegoer.widen(treegoer=treegoer,
species=globunt$Switchboard,
filter.vars=focal.vars)## bio12 - ranges for all species: TRUE
## climaticMoistureIndex - ranges for all species: TRUE
## monthCountByTemp10 - ranges for all species: TRUE
## growingDegDays5 - ranges for all species: TRUE
## bio05 - ranges for all species: TRUE
## bio06 - ranges for all species: TRUE
## bio16 - ranges for all species: TRUE
## bio17 - ranges for all species: TRUE
## MCWD - ranges for all species: TRUEnames(treegoer.wide)## [1] "species" "bio01_n"
## [3] "bio01_MIN" "bio01_Q05"
## [5] "bio01_QRT1" "bio01_QRT3"
## [7] "bio01_Q95" "bio01_MAX"
## [9] "bio12_n" "bio12_MIN"
## [11] "bio12_Q05" "bio12_QRT1"
## [13] "bio12_QRT3" "bio12_Q95"
## [15] "bio12_MAX" "climaticMoistureIndex_n"
## [17] "climaticMoistureIndex_MIN" "climaticMoistureIndex_Q05"
## [19] "climaticMoistureIndex_QRT1" "climaticMoistureIndex_QRT3"
## [21] "climaticMoistureIndex_Q95" "climaticMoistureIndex_MAX"
## [23] "monthCountByTemp10_n" "monthCountByTemp10_MIN"
## [25] "monthCountByTemp10_Q05" "monthCountByTemp10_QRT1"
## [27] "monthCountByTemp10_QRT3" "monthCountByTemp10_Q95"
## [29] "monthCountByTemp10_MAX" "growingDegDays5_n"
## [31] "growingDegDays5_MIN" "growingDegDays5_Q05"
## [33] "growingDegDays5_QRT1" "growingDegDays5_QRT3"
## [35] "growingDegDays5_Q95" "growingDegDays5_MAX"
## [37] "bio05_n" "bio05_MIN"
## [39] "bio05_Q05" "bio05_QRT1"
## [41] "bio05_QRT3" "bio05_Q95"
## [43] "bio05_MAX" "bio06_n"
## [45] "bio06_MIN" "bio06_Q05"
## [47] "bio06_QRT1" "bio06_QRT3"
## [49] "bio06_Q95" "bio06_MAX"
## [51] "bio16_n" "bio16_MIN"
## [53] "bio16_Q05" "bio16_QRT1"
## [55] "bio16_QRT3" "bio16_Q95"
## [57] "bio16_MAX" "bio17_n"
## [59] "bio17_MIN" "bio17_Q05"
## [61] "bio17_QRT1" "bio17_QRT3"
## [63] "bio17_Q95" "bio17_MAX"
## [65] "MCWD_n" "MCWD_MIN"
## [67] "MCWD_Q05" "MCWD_QRT1"
## [69] "MCWD_QRT3" "MCWD_Q95"
## [71] "MCWD_MAX"For the first filtering exercise, we will only use the mean annual temperature (BIO01). This is the same bioclimatic variable that is used in the BGCI Climate Assessment Tool.
Similar to internal calculations in GlobUNT, the list of species is filtered sequentially for the minimum - maximum range (score = 1), 5% - 95% range (score = 2) and 25% - 75% range (score = 3).
PL.vars <- "bio01" # PL = Planting LocationFirst we filter for the baseline climate, selecting the Dagoretti weather station location in Nairobi …
site.planting <- site.base[site.base$Station == "Nairobi / Dagoretti", ]
site.planting[, PL.vars]## [1] 18.93… and then calculating the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 92nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 132nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 112nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 109nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 92head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 3 Acokanthera oppositifolia 3 117
## 4 Acokanthera schimperi 3 56
## 9 Alangium chinense 3 443
## 16 Albizia gummifera 3 331
## 22 Aloe volkensii 3 30
## 24 Anthocleista grandiflora 3 170tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 430 Vangueria infausta 3 428
## 432 Vangueria volkensii 3 61
## 433 Vepris nobilis 3 148
## 445 Volkameria glabra 3 215
## 447 Warburgia ugandensis 3 47
## 454 Xymalos monospora 3 321Almost the same script can be used for filter species suitable for the future climate. The only modification is that we now obtain the bioclimatic conditions from the future climate data set.
site.planting <- site.fut[site.fut$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## [,1]
## [1,] 21… and then proceed with the same script to calculate the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 134nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 186nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 66nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 59nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 134head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 1 Acalypha fruticosa 3 123
## 2 Acalypha ornata 3 240
## 4 Acokanthera schimperi 3 56
## 13 Albizia anthelmintica 3 218
## 16 Albizia gummifera 3 331
## 22 Aloe volkensii 3 30tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 447 Warburgia ugandensis 3 47
## 450 Ximenia caffra 3 339
## 455 Zanha africana 3 47
## 456 Zanthoxylum chalybeum 3 42
## 460 Ziziphus mucronata 3 1129
## 461 Ziziphus spina-christi 3 819In the second filtering exercise, we filter the species for three environmental variables, a growing degree days variable (GDD5), the minimum temperature of the coldest month (BIO06) and a moisture index (CMI). Similar environmental variables have been used previously in research on the adaptation of tree species to future climates (see Booth 2016).
The same scripts can be used after selecting the variables.
PL.vars <- c("growingDegDays5", "bio06", "climaticMoistureIndex") # PL = Planting Location
PL.vars %in% focal.vars # checking if we had selected these variables earlier## [1] TRUE TRUE TRUEsite.planting <- site.base[site.base$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## 8208
## growingDegDays5 5085.00
## bio06 11.70
## climaticMoistureIndex -0.34… and then calculating the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 30nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 174nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 123nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 118nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 30head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 22 Aloe volkensii 3 30
## 24 Anthocleista grandiflora 3 170
## 69 Cassipourea malosana 3 138
## 74 Clausena anisata 3 848
## 112 Croton megalocarpus 3 69
## 145 Drypetes gerrardii 3 132tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 371 Solanum giganteum 3 136
## 390 Syzygium cordatum 3 445
## 429 Vangueria apiculata 3 91
## 432 Vangueria volkensii 3 61
## 433 Vepris nobilis 3 148
## 447 Warburgia ugandensis 3 47Again modifying the script is easy, again first selecting the same weather station location:
site.planting <- site.fut[site.fut$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## 8208
## growingDegDays5 5787.75
## bio06 13.90
## climaticMoistureIndex -0.22… and then proceed with the same script to calculate the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 44nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 211nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 106nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 84nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 44head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 2 Acalypha ornata 3 240
## 16 Albizia gummifera 3 331
## 22 Aloe volkensii 3 30
## 40 Bersama abyssinica 3 348
## 47 Brachystegia spiciformis 3 413
## 51 Bridelia micrantha 3 705tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 416 Uapaca nitida 3 118
## 429 Vangueria apiculata 3 91
## 431 Vangueria madagascariensis 3 220
## 433 Vepris nobilis 3 148
## 437 Vitex fischeri 3 26
## 447 Warburgia ugandensis 3 47As you will have expected by now, it is easy to modify the script to include more variables.
Now we analyze for all variables included in GlobUNT.
PL.vars <- focal.vars # all the variablessite.planting <- site.base[site.base$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## 8208
## bio01 18.93
## bio12 1000.00
## climaticMoistureIndex -0.34
## monthCountByTemp10 12.00
## growingDegDays5 5085.00
## bio05 27.60
## bio06 11.70
## bio16 505.00
## bio17 69.00
## MCWD -418.50… and then calculating the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 17nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 146nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 144nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 138nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 17head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 22 Aloe volkensii 3 30
## 24 Anthocleista grandiflora 3 170
## 69 Cassipourea malosana 3 138
## 112 Croton megalocarpus 3 69
## 145 Drypetes gerrardii 3 132
## 150 Englerophytum natalense 3 88tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 317 Psychotria capensis 3 268
## 325 Rhamnus staddo 3 53
## 334 Rotheca myricoides 3 410
## 371 Solanum giganteum 3 136
## 390 Syzygium cordatum 3 445
## 429 Vangueria apiculata 3 91site.planting <- site.fut[site.fut$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## 8208
## bio01 21.00
## bio12 1062.00
## climaticMoistureIndex -0.22
## monthCountByTemp10 12.00
## growingDegDays5 5787.75
## bio05 29.10
## bio06 13.90
## bio16 509.00
## bio17 67.00
## MCWD -475.70… and then calculating the climate score
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)
nrow(treegoer.scores)## [1] 461# breakdown
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ]) # species in the middle of the range## [1] 21nrow(treegoer.scores[treegoer.scores$climate.score == 2, ]) # species between 5% - 25% or 75% - 95%## [1] 187nrow(treegoer.scores[treegoer.scores$climate.score == 1, ]) # species between 0%-5% or 95% - 100%## [1] 144nrow(treegoer.scores[treegoer.scores$climate.score == 0, ]) # species < 0% or > 100% (planting site has novel conditions)## [1] 93nrow(treegoer.scores[treegoer.scores$climate.score == -1, ]) # species not documented in TreeGOER## [1] 16The following species got the highest scores:
nrow(treegoer.scores[treegoer.scores$climate.score == 3, ])## [1] 21head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 22 Aloe volkensii 3 30
## 51 Bridelia micrantha 3 705
## 74 Clausena anisata 3 848
## 97 Cordia africana 3 181
## 111 Croton macrostachyus 3 234
## 113 Croton sylvaticus 3 193tail(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER
## 323 Rawsonia lucida 3 185
## 382 Strychnos henningsii 3 138
## 395 Tabernaemontana ventricosa 3 111
## 412 Trichilia dregeana 3 128
## 431 Vangueria madagascariensis 3 220
## 433 Vepris nobilis 3 148For the downloaded species list from GlobUNT (available via the
Here I show how these calculations can be repeated for the baseline climate.
site.planting <- site.base[site.base$Station == "Nairobi / Dagoretti", ]
t(site.planting[, PL.vars])## 8208
## bio01 18.93
## bio12 1000.00
## climaticMoistureIndex -0.34
## monthCountByTemp10 12.00
## growingDegDays5 5085.00
## bio05 27.60
## bio06 11.70
## bio16 505.00
## bio17 69.00
## MCWD -418.50First calculate the climate score again:
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)Next loop through all the selected variables:
for (i in 1:length(PL.vars)) {
# i <- 1
PL.var <- PL.vars[i]
treegoer.scores2 <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.var,
site.data=site.planting)
treegoer.scores2 <- treegoer.scores2[, c(1:2)]
names(treegoer.scores2)[2] <- paste0(PL.var, "_score")
treegoer.scores <- left_join(treegoer.scores,
treegoer.scores2,
by="species")
}What can be seen now is which variable were critical in obtaining a lower score. For species with the highest scores, they will have obtained the highest score for each individual variable. For species of lower scores, the lower score will have been obtained for at least some of the individual variables.
summary(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER bio01_score bio12_score
## Length:17 Min. :3 Min. : 26.0 Min. :3 Min. :3
## Class :character 1st Qu.:3 1st Qu.: 69.0 1st Qu.:3 1st Qu.:3
## Mode :character Median :3 Median :132.0 Median :3 Median :3
## Mean :3 Mean :161.1 Mean :3 Mean :3
## 3rd Qu.:3 3rd Qu.:170.0 3rd Qu.:3 3rd Qu.:3
## Max. :3 Max. :445.0 Max. :3 Max. :3
## climaticMoistureIndex_score monthCountByTemp10_score growingDegDays5_score
## Min. :3 Min. :3 Min. :3
## 1st Qu.:3 1st Qu.:3 1st Qu.:3
## Median :3 Median :3 Median :3
## Mean :3 Mean :3 Mean :3
## 3rd Qu.:3 3rd Qu.:3 3rd Qu.:3
## Max. :3 Max. :3 Max. :3
## bio05_score bio06_score bio16_score bio17_score MCWD_score
## Min. :3 Min. :3 Min. :3 Min. :3 Min. :3
## 1st Qu.:3 1st Qu.:3 1st Qu.:3 1st Qu.:3 1st Qu.:3
## Median :3 Median :3 Median :3 Median :3 Median :3
## Mean :3 Mean :3 Mean :3 Mean :3 Mean :3
## 3rd Qu.:3 3rd Qu.:3 3rd Qu.:3 3rd Qu.:3 3rd Qu.:3
## Max. :3 Max. :3 Max. :3 Max. :3 Max. :3head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER bio01_score bio12_score
## 22 Aloe volkensii 3 30 3 3
## 24 Anthocleista grandiflora 3 170 3 3
## 69 Cassipourea malosana 3 138 3 3
## 112 Croton megalocarpus 3 69 3 3
## 145 Drypetes gerrardii 3 132 3 3
## 150 Englerophytum natalense 3 88 3 3
## climaticMoistureIndex_score monthCountByTemp10_score growingDegDays5_score
## 22 3 3 3
## 24 3 3 3
## 69 3 3 3
## 112 3 3 3
## 145 3 3 3
## 150 3 3 3
## bio05_score bio06_score bio16_score bio17_score MCWD_score
## 22 3 3 3 3 3
## 24 3 3 3 3 3
## 69 3 3 3 3 3
## 112 3 3 3 3 3
## 145 3 3 3 3 3
## 150 3 3 3 3 3summary(treegoer.scores[treegoer.scores$climate.score == 2, ])## species climate.score n.TreeGOER bio01_score
## Length:146 Min. :2 Min. : 6.00 Min. :2.000
## Class :character 1st Qu.:2 1st Qu.: 83.25 1st Qu.:2.000
## Mode :character Median :2 Median : 200.50 Median :2.000
## Mean :2 Mean : 514.72 Mean :2.438
## 3rd Qu.:2 3rd Qu.: 430.75 3rd Qu.:3.000
## Max. :2 Max. :20896.00 Max. :3.000
## bio12_score climaticMoistureIndex_score monthCountByTemp10_score
## Min. :2.000 Min. :2.000 Min. :3
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3
## Median :3.000 Median :3.000 Median :3
## Mean :2.548 Mean :2.582 Mean :3
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3
## Max. :3.000 Max. :3.000 Max. :3
## growingDegDays5_score bio05_score bio06_score bio16_score
## Min. :2.000 Min. :2.000 Min. :2.000 Min. :2.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.432 Mean :2.527 Mean :2.589 Mean :2.719
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
## bio17_score MCWD_score
## Min. :2.000 Min. :2.000
## 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000
## Mean :2.664 Mean :2.541
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :3.000head(treegoer.scores[treegoer.scores$climate.score == 2, ])## species climate.score n.TreeGOER bio01_score bio12_score
## 1 Acalypha fruticosa 2 123 2 2
## 2 Acalypha ornata 2 240 2 3
## 4 Acokanthera schimperi 2 56 3 3
## 16 Albizia gummifera 2 331 3 2
## 26 Antidesma venosum 2 914 2 3
## 29 Avicennia marina 2 1501 3 3
## climaticMoistureIndex_score monthCountByTemp10_score growingDegDays5_score
## 1 2 3 2
## 2 3 3 2
## 4 2 3 3
## 16 2 3 3
## 26 3 3 2
## 29 2 3 3
## bio05_score bio06_score bio16_score bio17_score MCWD_score
## 1 2 3 2 3 2
## 2 2 3 3 2 2
## 4 3 3 2 3 2
## 16 3 3 2 3 3
## 26 2 2 3 2 3
## 29 3 3 3 3 3summary(treegoer.scores[treegoer.scores$climate.score == 1, ])## species climate.score n.TreeGOER bio01_score
## Length:144 Min. :1 Min. : 13.0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 159.8 1st Qu.:1.000
## Mode :character Median :1 Median : 342.0 Median :1.000
## Mean :1 Mean : 1040.3 Mean :1.382
## 3rd Qu.:1 3rd Qu.: 705.8 3rd Qu.:2.000
## Max. :1 Max. :39616.0 Max. :3.000
## bio12_score climaticMoistureIndex_score monthCountByTemp10_score
## Min. :1.000 Min. :1.000 Min. :2.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :2.000 Median :2.000 Median :3.000
## Mean :2.097 Mean :2.153 Mean :2.993
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :3.000 Max. :3.000
## growingDegDays5_score bio05_score bio06_score bio16_score
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :1.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.382 Mean :1.667 Mean :2.035 Mean :2.403
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :3.000 Max. :3.000 Max. :3.000
## bio17_score MCWD_score
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :2.000
## Mean :2.174 Mean :2.028
## 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.000 Max. :3.000head(treegoer.scores[treegoer.scores$climate.score == 1, ])## species climate.score n.TreeGOER bio01_score bio12_score
## 3 Acokanthera oppositifolia 1 117 3 2
## 5 Adansonia digitata 1 2218 1 3
## 6 Adenium obesum 1 594 1 2
## 7 Afrocanthium lactescens 1 52 2 3
## 8 Afzelia quanzensis 1 252 1 3
## 9 Alangium chinense 1 443 3 2
## climaticMoistureIndex_score monthCountByTemp10_score growingDegDays5_score
## 3 2 3 3
## 5 2 3 1
## 6 2 3 1
## 7 3 3 2
## 8 3 3 1
## 9 1 3 3
## bio05_score bio06_score bio16_score bio17_score MCWD_score
## 3 3 1 2 3 2
## 5 1 2 3 1 2
## 6 1 3 2 2 2
## 7 3 3 3 2 1
## 8 1 3 3 2 1
## 9 3 2 2 3 2summary(treegoer.scores[treegoer.scores$climate.score == 0, ])## species climate.score n.TreeGOER bio01_score
## Length:138 Min. :0 Min. : 1.0 Min. :0.0000
## Class :character 1st Qu.:0 1st Qu.: 22.0 1st Qu.:0.0000
## Mode :character Median :0 Median : 53.5 Median :0.0000
## Mean :0 Mean : 301.1 Mean :0.3333
## 3rd Qu.:0 3rd Qu.: 320.8 3rd Qu.:0.0000
## Max. :0 Max. :3499.0 Max. :3.0000
## bio12_score climaticMoistureIndex_score monthCountByTemp10_score
## Min. :0.00 Min. :0.000 Min. :3
## 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:3
## Median :2.00 Median :2.000 Median :3
## Mean :1.87 Mean :1.862 Mean :3
## 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:3
## Max. :3.00 Max. :3.000 Max. :3
## growingDegDays5_score bio05_score bio06_score bio16_score
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:2.000
## Median :0.0000 Median :0.0000 Median :1.000 Median :2.000
## Mean :0.3333 Mean :0.6739 Mean :1.007 Mean :2.123
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.:3.000
## Max. :3.0000 Max. :3.0000 Max. :3.000 Max. :3.000
## bio17_score MCWD_score
## Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:0.000
## Median :2.000 Median :1.000
## Mean :1.862 Mean :1.261
## 3rd Qu.:3.000 3rd Qu.:2.000
## Max. :3.000 Max. :3.000head(treegoer.scores[treegoer.scores$climate.score == 0, ])## species climate.score n.TreeGOER bio01_score bio12_score
## 10 Alangium salviifolium 0 48 0 2
## 15 Albizia glaberrima 0 365 0 3
## 19 Albizia zygia 0 1198 0 2
## 25 Antiaris toxicaria 0 1150 0 2
## 28 Asteranthe asterias 0 73 0 2
## 32 Balanites aegyptiaca 0 3499 0 2
## climaticMoistureIndex_score monthCountByTemp10_score growingDegDays5_score
## 10 3 3 0
## 15 3 3 0
## 19 3 3 0
## 25 3 3 0
## 28 2 3 0
## 32 1 3 0
## bio05_score bio06_score bio16_score bio17_score MCWD_score
## 10 0 2 2 3 2
## 15 0 1 3 2 3
## 19 0 1 3 2 3
## 25 0 1 3 2 3
## 28 0 0 3 3 3
## 32 0 1 2 0 1In the April 2024 version of GlobUNT, when the user downloads the species list, information is also provided on the position of the planting site with respect to the median and extremes (minimum or maximum) of the species range.
It may be important to differentiate between situations where the environmental conditions of the planting site are above or below the median of the species range. Where the climate score is zero as a result from encountering novel conditions for annual precipitation (BIO12), it may matter less if the planting site receives a higher amount of rainfall than maximally observed for the species, whereas it may be critical that the site does not receive a lower amount of rainfally than minimally observed for the species. For the maximum temperature of the warmest month, it may especially be critical that not a higher temperature is observed for the planting site, whereas a species maybe can survive in conditions where the maximum temperature is below the minimum observed.
The calculation of the new metric for the position of the planting site is done as follows:
For sites where the conditions of the planting location (PL) are above the median, the position is calculated as:
(PL - MEDIAN) / (MAX - MEDIAN)
For sites where the conditions of the planting location (PL) are below the median, the position is calculated as:
(PL - MEDIAN) / (MEDIAN - MIN)
The sign (positive or negative) will therefore indicate the position (respectively, lower or higher) of the planting site with respect to the median of the species.
The magnitude of the metric will indicate the relative distance of the planting site with respect to the difference between median and extreme. Values that are lower than -1 will indicate novel conditions below the minimum. Values that are above +1 will indicate novel conditions above the maximum.
The analysis requires a new function treegoer.position(). My plan is to integrate the new function in a next version of the BiodiversityR package. But for the time being, you need to define the functions here.
`treegoer.position` <- function(site.data, treegoer.wide, focal.var) {
focal.test <- paste0(focal.var, c("_MIN", "_MEDIAN", "_MAX"))
focal.test <- c("species", focal.test)
data1 <- treegoer.wide[, ..focal.test]
names(data1) <- c("species", "MIN", "MEDIAN", "MAX")
data1$position <- as.numeric(site.data[, focal.var]) - data1$MEDIAN
data1$position_score <- ifelse(
data1$position >= 0,
data1$position / (data1$MAX - data1$MEDIAN),
data1$position / (data1$MEDIAN - data1$MIN)
)
data1$position_score <- round(data1$position_score, 2)
out.names <- c("species", "position_score")
data1 <- data1[, ..out.names]
names(data1)[2] <- paste0(focal.var, "_position")
return(data1)
}What is further needed is that the treegoer.widen() function also includes information on the median:
`treegoer.widen` <- function(
treegoer,
species=unique(treegoer$species)[1:100],
filter.vars=c("bio05", "bio14", "climaticMoistureIndex")
)
{
treegoer.subset <- treegoer$species %in% species
treegoer <- treegoer[treegoer.subset, ]
focal.treegoer <- filter.vars[1]
focal.ranges <- treegoer[treegoer$var == focal.treegoer,
c("species", "n", "MIN", "Q05", "QRT1", "MEDIAN", "QRT3", "Q95", "MAX")]
names(focal.ranges)[2:9] <- paste0(focal.treegoer, "_", names(focal.ranges)[2:9])
ranges.lookup <- focal.ranges
for (i in 2:length(filter.vars)) {
focal.treegoer <- filter.vars[i]
focal.ranges <- treegoer[treegoer$var == focal.treegoer,
c("species", "n", "MIN", "Q05", "QRT1", "MEDIAN", "QRT3", "Q95", "MAX")]
names(focal.ranges)[2:9] <- paste0(focal.treegoer, "_", names(focal.ranges)[2:9])
# check - note that data was missing for some explanatory variables especially soil
cat(paste(focal.treegoer, "- ranges for all species:", all.equal(focal.ranges$species, ranges.lookup$species), "\n"))
ranges.lookup <- dplyr::left_join(ranges.lookup,
focal.ranges,
by="species")
# ranges.lookup <- cbind(ranges.lookup, focal.ranges[, c(2:8)]) # used in the Rpub, can not handle
}
return(ranges.lookup)
}Now we create the new data set, repeating the scripts seen in Section 4 previously:
treegoer.wide <- treegoer.widen(treegoer=treegoer,
species=globunt$Switchboard,
filter.vars=focal.vars)## bio12 - ranges for all species: TRUE
## climaticMoistureIndex - ranges for all species: TRUE
## monthCountByTemp10 - ranges for all species: TRUE
## growingDegDays5 - ranges for all species: TRUE
## bio05 - ranges for all species: TRUE
## bio06 - ranges for all species: TRUE
## bio16 - ranges for all species: TRUE
## bio17 - ranges for all species: TRUE
## MCWD - ranges for all species: TRUEnames(treegoer.wide)## [1] "species" "bio01_n"
## [3] "bio01_MIN" "bio01_Q05"
## [5] "bio01_QRT1" "bio01_MEDIAN"
## [7] "bio01_QRT3" "bio01_Q95"
## [9] "bio01_MAX" "bio12_n"
## [11] "bio12_MIN" "bio12_Q05"
## [13] "bio12_QRT1" "bio12_MEDIAN"
## [15] "bio12_QRT3" "bio12_Q95"
## [17] "bio12_MAX" "climaticMoistureIndex_n"
## [19] "climaticMoistureIndex_MIN" "climaticMoistureIndex_Q05"
## [21] "climaticMoistureIndex_QRT1" "climaticMoistureIndex_MEDIAN"
## [23] "climaticMoistureIndex_QRT3" "climaticMoistureIndex_Q95"
## [25] "climaticMoistureIndex_MAX" "monthCountByTemp10_n"
## [27] "monthCountByTemp10_MIN" "monthCountByTemp10_Q05"
## [29] "monthCountByTemp10_QRT1" "monthCountByTemp10_MEDIAN"
## [31] "monthCountByTemp10_QRT3" "monthCountByTemp10_Q95"
## [33] "monthCountByTemp10_MAX" "growingDegDays5_n"
## [35] "growingDegDays5_MIN" "growingDegDays5_Q05"
## [37] "growingDegDays5_QRT1" "growingDegDays5_MEDIAN"
## [39] "growingDegDays5_QRT3" "growingDegDays5_Q95"
## [41] "growingDegDays5_MAX" "bio05_n"
## [43] "bio05_MIN" "bio05_Q05"
## [45] "bio05_QRT1" "bio05_MEDIAN"
## [47] "bio05_QRT3" "bio05_Q95"
## [49] "bio05_MAX" "bio06_n"
## [51] "bio06_MIN" "bio06_Q05"
## [53] "bio06_QRT1" "bio06_MEDIAN"
## [55] "bio06_QRT3" "bio06_Q95"
## [57] "bio06_MAX" "bio16_n"
## [59] "bio16_MIN" "bio16_Q05"
## [61] "bio16_QRT1" "bio16_MEDIAN"
## [63] "bio16_QRT3" "bio16_Q95"
## [65] "bio16_MAX" "bio17_n"
## [67] "bio17_MIN" "bio17_Q05"
## [69] "bio17_QRT1" "bio17_MEDIAN"
## [71] "bio17_QRT3" "bio17_Q95"
## [73] "bio17_MAX" "MCWD_n"
## [75] "MCWD_MIN" "MCWD_Q05"
## [77] "MCWD_QRT1" "MCWD_MEDIAN"
## [79] "MCWD_QRT3" "MCWD_Q95"
## [81] "MCWD_MAX"First calculate the climate score again:
treegoer.scores <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.vars,
site.data=site.planting)Next loop through all the selected variables, calcuting individual scores and positions:
for (i in 1:length(PL.vars)) {
# i <- 1
PL.var <- PL.vars[i]
treegoer.scores2 <- treegoer.score(site.species=globunt$Switchboard,
treegoer.wide=treegoer.wide,
filter.vars=PL.var,
site.data=site.planting)
treegoer.scores2 <- treegoer.scores2[, c(1:2)]
names(treegoer.scores2)[2] <- paste0(PL.var, "_score")
treegoer.scores <- left_join(treegoer.scores,
treegoer.scores2,
by="species")
treegoer.positions <- treegoer.position(site.data=site.planting,
treegoer.wide=treegoer.wide,
focal.var=PL.var)
treegoer.scores <- left_join(treegoer.scores,
treegoer.positions,
by="species")
}What can be seen now are the positions of the planting site for each variable:
summary(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER bio01_score
## Length:17 Min. :3 Min. : 26.0 Min. :3
## Class :character 1st Qu.:3 1st Qu.: 69.0 1st Qu.:3
## Mode :character Median :3 Median :132.0 Median :3
## Mean :3 Mean :161.1 Mean :3
## 3rd Qu.:3 3rd Qu.:170.0 3rd Qu.:3
## Max. :3 Max. :445.0 Max. :3
##
## bio01_position bio12_score bio12_position climaticMoistureIndex_score
## Min. :-0.25000 Min. :3 Min. :-0.31000 Min. :3
## 1st Qu.:-0.17000 1st Qu.:3 1st Qu.:-0.14000 1st Qu.:3
## Median :-0.09000 Median :3 Median :-0.06000 Median :3
## Mean :-0.09529 Mean :3 Mean :-0.06882 Mean :3
## 3rd Qu.:-0.04000 3rd Qu.:3 3rd Qu.: 0.02000 3rd Qu.:3
## Max. : 0.17000 Max. :3 Max. : 0.12000 Max. :3
##
## climaticMoistureIndex_position monthCountByTemp10_score
## Min. :-0.4100 Min. :3
## 1st Qu.:-0.2400 1st Qu.:3
## Median :-0.1400 Median :3
## Mean :-0.1471 Mean :3
## 3rd Qu.: 0.0000 3rd Qu.:3
## Max. : 0.0700 Max. :3
##
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## Min. : NA Min. :3 Min. :-0.25000
## 1st Qu.: NA 1st Qu.:3 1st Qu.:-0.17000
## Median : NA Median :3 Median :-0.09000
## Mean :NaN Mean :3 Mean :-0.09471
## 3rd Qu.: NA 3rd Qu.:3 3rd Qu.:-0.04000
## Max. : NA Max. :3 Max. : 0.17000
## NA's :17
## bio05_score bio05_position bio06_score bio06_position bio16_score
## Min. :3 Min. :-0.20000 Min. :3 Min. :-0.24000 Min. :3
## 1st Qu.:3 1st Qu.:-0.03000 1st Qu.:3 1st Qu.: 0.04000 1st Qu.:3
## Median :3 Median : 0.04000 Median :3 Median : 0.11000 Median :3
## Mean :3 Mean : 0.04294 Mean :3 Mean : 0.07412 Mean :3
## 3rd Qu.:3 3rd Qu.: 0.10000 3rd Qu.:3 3rd Qu.: 0.16000 3rd Qu.:3
## Max. :3 Max. : 0.29000 Max. :3 Max. : 0.31000 Max. :3
##
## bio16_position bio17_score bio17_position MCWD_score
## Min. :-0.03000 Min. :3 Min. :-0.540000 Min. :3
## 1st Qu.: 0.01000 1st Qu.:3 1st Qu.:-0.030000 1st Qu.:3
## Median : 0.12000 Median :3 Median : 0.020000 Median :3
## Mean : 0.09235 Mean :3 Mean :-0.004118 Mean :3
## 3rd Qu.: 0.16000 3rd Qu.:3 3rd Qu.: 0.070000 3rd Qu.:3
## Max. : 0.21000 Max. :3 Max. : 0.270000 Max. :3
##
## MCWD_position
## Min. :-0.1300
## 1st Qu.:-0.0100
## Median : 0.1900
## Mean : 0.1529
## 3rd Qu.: 0.2800
## Max. : 0.4100
## head(treegoer.scores[treegoer.scores$climate.score == 3, ])## species climate.score n.TreeGOER bio01_score
## 22 Aloe volkensii 3 30 3
## 24 Anthocleista grandiflora 3 170 3
## 69 Cassipourea malosana 3 138 3
## 112 Croton megalocarpus 3 69 3
## 145 Drypetes gerrardii 3 132 3
## 150 Englerophytum natalense 3 88 3
## bio01_position bio12_score bio12_position climaticMoistureIndex_score
## 22 -0.25 3 0.00 3
## 24 -0.17 3 -0.06 3
## 69 -0.04 3 -0.12 3
## 112 -0.07 3 -0.21 3
## 145 -0.18 3 -0.14 3
## 150 -0.16 3 -0.08 3
## climaticMoistureIndex_position monthCountByTemp10_score
## 22 0.00 3
## 24 -0.15 3
## 69 -0.29 3
## 112 -0.21 3
## 145 -0.24 3
## 150 -0.31 3
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## 22 NaN 3 -0.25
## 24 NaN 3 -0.17
## 69 NaN 3 -0.04
## 112 NaN 3 -0.07
## 145 NaN 3 -0.18
## 150 NaN 3 -0.16
## bio05_score bio05_position bio06_score bio06_position bio16_score
## 22 3 -0.08 3 -0.24 3
## 24 3 -0.03 3 0.25 3
## 69 3 0.10 3 0.14 3
## 112 3 0.06 3 -0.13 3
## 145 3 0.07 3 0.07 3
## 150 3 0.04 3 0.13 3
## bio16_position bio17_score bio17_position MCWD_score MCWD_position
## 22 0.16 3 -0.19 3 0.41
## 24 0.07 3 0.10 3 0.19
## 69 0.01 3 0.02 3 -0.01
## 112 -0.01 3 -0.21 3 -0.06
## 145 0.12 3 -0.03 3 -0.04
## 150 0.21 3 0.02 3 0.01summary(treegoer.scores[treegoer.scores$climate.score == 2, ])## species climate.score n.TreeGOER bio01_score
## Length:146 Min. :2 Min. : 6.00 Min. :2.000
## Class :character 1st Qu.:2 1st Qu.: 83.25 1st Qu.:2.000
## Mode :character Median :2 Median : 200.50 Median :2.000
## Mean :2 Mean : 514.72 Mean :2.438
## 3rd Qu.:2 3rd Qu.: 430.75 3rd Qu.:3.000
## Max. :2 Max. :20896.00 Max. :3.000
##
## bio01_position bio12_score bio12_position
## Min. :-0.8600 Min. :2.000 Min. :-0.85000
## 1st Qu.:-0.4300 1st Qu.:2.000 1st Qu.:-0.25750
## Median :-0.2500 Median :3.000 Median :-0.09000
## Mean :-0.2132 Mean :2.548 Mean :-0.08068
## 3rd Qu.: 0.0275 3rd Qu.:3.000 3rd Qu.: 0.14000
## Max. : 0.9200 Max. :3.000 Max. : 0.55000
##
## climaticMoistureIndex_score climaticMoistureIndex_position
## Min. :2.000 Min. :-0.8600
## 1st Qu.:2.000 1st Qu.:-0.3400
## Median :3.000 Median :-0.0750
## Mean :2.582 Mean :-0.1008
## 3rd Qu.:3.000 3rd Qu.: 0.1175
## Max. :3.000 Max. : 0.5500
##
## monthCountByTemp10_score monthCountByTemp10_position growingDegDays5_score
## Min. :3 Min. : NA Min. :2.000
## 1st Qu.:3 1st Qu.: NA 1st Qu.:2.000
## Median :3 Median : NA Median :2.000
## Mean :3 Mean :NaN Mean :2.432
## 3rd Qu.:3 3rd Qu.: NA 3rd Qu.:3.000
## Max. :3 Max. : NA Max. :3.000
## NA's :146
## growingDegDays5_position bio05_score bio05_position bio06_score
## Min. :-0.8600 Min. :2.000 Min. :-0.8100 Min. :2.000
## 1st Qu.:-0.4300 1st Qu.:2.000 1st Qu.:-0.3700 1st Qu.:2.000
## Median :-0.2450 Median :3.000 Median :-0.1650 Median :3.000
## Mean :-0.2126 Mean :2.527 Mean :-0.1466 Mean :2.589
## 3rd Qu.: 0.0350 3rd Qu.:3.000 3rd Qu.: 0.0400 3rd Qu.:3.000
## Max. : 0.9200 Max. :3.000 Max. : 0.9200 Max. :3.000
##
## bio06_position bio16_score bio16_position bio17_score
## Min. :-0.85000 Min. :2.000 Min. :-0.57000 Min. :2.000
## 1st Qu.:-0.26000 1st Qu.:2.000 1st Qu.:-0.13750 1st Qu.:2.000
## Median :-0.04000 Median :3.000 Median : 0.01500 Median :3.000
## Mean :-0.02336 Mean :2.719 Mean : 0.02185 Mean :2.664
## 3rd Qu.: 0.22000 3rd Qu.:3.000 3rd Qu.: 0.20750 3rd Qu.:3.000
## Max. : 0.76000 Max. :3.000 Max. : 0.78000 Max. :3.000
##
## bio17_position MCWD_score MCWD_position
## Min. :-0.58000 Min. :2.000 Min. :-0.4800
## 1st Qu.: 0.01250 1st Qu.:2.000 1st Qu.: 0.0400
## Median : 0.09000 Median :3.000 Median : 0.3150
## Mean : 0.05699 Mean :2.541 Mean : 0.2581
## 3rd Qu.: 0.18000 3rd Qu.:3.000 3rd Qu.: 0.4500
## Max. : 0.94000 Max. :3.000 Max. : 0.8300
## head(treegoer.scores[treegoer.scores$climate.score == 2, ])## species climate.score n.TreeGOER bio01_score bio01_position
## 1 Acalypha fruticosa 2 123 2 -0.41
## 2 Acalypha ornata 2 240 2 -0.37
## 4 Acokanthera schimperi 2 56 3 -0.10
## 16 Albizia gummifera 2 331 3 -0.14
## 26 Antidesma venosum 2 914 2 -0.54
## 29 Avicennia marina 2 1501 3 0.07
## bio12_score bio12_position climaticMoistureIndex_score
## 1 2 0.30 2
## 2 3 -0.14 3
## 4 3 0.14 2
## 16 2 -0.46 2
## 26 3 -0.11 3
## 29 3 -0.19 2
## climaticMoistureIndex_position monthCountByTemp10_score
## 1 0.34 3
## 2 -0.07 3
## 4 0.18 3
## 16 -0.48 3
## 26 -0.02 3
## 29 -0.37 3
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## 1 NaN 2 -0.41
## 2 NaN 2 -0.36
## 4 NaN 3 -0.10
## 16 NaN 3 -0.14
## 26 NaN 2 -0.54
## 29 NaN 3 0.07
## bio05_score bio05_position bio06_score bio06_position bio16_score
## 1 2 -0.36 3 -0.20 2
## 2 2 -0.24 3 -0.19 3
## 4 3 -0.09 3 0.13 2
## 16 3 -0.02 3 -0.09 2
## 26 2 -0.46 2 -0.33 3
## 29 3 -0.02 3 0.23 3
## bio16_position bio17_score bio17_position MCWD_score MCWD_position
## 1 0.39 3 0.21 2 0.63
## 2 -0.03 2 0.19 2 0.38
## 4 0.22 3 0.14 2 0.58
## 16 -0.32 3 -0.03 3 0.03
## 26 -0.13 2 0.06 3 0.38
## 29 0.03 3 -0.57 3 -0.13summary(treegoer.scores[treegoer.scores$climate.score == 1, ])## species climate.score n.TreeGOER bio01_score
## Length:144 Min. :1 Min. : 13.0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 159.8 1st Qu.:1.000
## Mode :character Median :1 Median : 342.0 Median :1.000
## Mean :1 Mean : 1040.3 Mean :1.382
## 3rd Qu.:1 3rd Qu.: 705.8 3rd Qu.:2.000
## Max. :1 Max. :39616.0 Max. :3.000
##
## bio01_position bio12_score bio12_position
## Min. :-1.0000 Min. :1.000 Min. :-0.9900
## 1st Qu.:-0.7700 1st Qu.:2.000 1st Qu.:-0.4325
## Median :-0.6100 Median :2.000 Median :-0.0550
## Mean :-0.5751 Mean :2.097 Mean :-0.1155
## 3rd Qu.:-0.4975 3rd Qu.:3.000 3rd Qu.: 0.1950
## Max. : 0.3900 Max. :3.000 Max. : 0.8200
##
## climaticMoistureIndex_score climaticMoistureIndex_position
## Min. :1.000 Min. :-0.92000
## 1st Qu.:2.000 1st Qu.:-0.38750
## Median :2.000 Median : 0.05000
## Mean :2.153 Mean :-0.05583
## 3rd Qu.:3.000 3rd Qu.: 0.22750
## Max. :3.000 Max. : 0.93000
##
## monthCountByTemp10_score monthCountByTemp10_position growingDegDays5_score
## Min. :2.000 Min. :1 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1 1st Qu.:1.000
## Median :3.000 Median :1 Median :1.000
## Mean :2.993 Mean :1 Mean :1.382
## 3rd Qu.:3.000 3rd Qu.:1 3rd Qu.:2.000
## Max. :3.000 Max. :1 Max. :3.000
## NA's :142
## growingDegDays5_position bio05_score bio05_position bio06_score
## Min. :-0.9900 Min. :1.000 Min. :-1.0000 Min. :1.000
## 1st Qu.:-0.7700 1st Qu.:1.000 1st Qu.:-0.6600 1st Qu.:2.000
## Median :-0.6100 Median :2.000 Median :-0.5250 Median :2.000
## Mean :-0.5747 Mean :1.667 Mean :-0.5078 Mean :2.035
## 3rd Qu.:-0.4900 3rd Qu.:2.000 3rd Qu.:-0.3900 3rd Qu.:3.000
## Max. : 0.4000 Max. :3.000 Max. : 0.3600 Max. :3.000
##
## bio06_position bio16_score bio16_position bio17_score
## Min. :-0.9500 Min. :1.000 Min. :-0.8700 Min. :1.000
## 1st Qu.:-0.5125 1st Qu.:2.000 1st Qu.:-0.3850 1st Qu.:2.000
## Median :-0.3400 Median :2.000 Median :-0.1300 Median :2.000
## Mean :-0.2668 Mean :2.403 Mean :-0.1102 Mean :2.174
## 3rd Qu.:-0.0725 3rd Qu.:3.000 3rd Qu.: 0.1225 3rd Qu.:3.000
## Max. : 0.7600 Max. :3.000 Max. : 0.8700 Max. :3.000
##
## bio17_position MCWD_score MCWD_position
## Min. :-0.7900 Min. :1.000 Min. :-0.4800
## 1st Qu.:-0.0525 1st Qu.:1.000 1st Qu.:-0.0450
## Median : 0.1550 Median :2.000 Median : 0.5050
## Mean : 0.1090 Mean :2.028 Mean : 0.3582
## 3rd Qu.: 0.3500 3rd Qu.:3.000 3rd Qu.: 0.6500
## Max. : 0.8000 Max. :3.000 Max. : 0.9700
## head(treegoer.scores[treegoer.scores$climate.score == 1, ])## species climate.score n.TreeGOER bio01_score bio01_position
## 3 Acokanthera oppositifolia 1 117 3 0.21
## 5 Adansonia digitata 1 2218 1 -0.60
## 6 Adenium obesum 1 594 1 -0.63
## 7 Afrocanthium lactescens 1 52 2 -0.28
## 8 Afzelia quanzensis 1 252 1 -0.50
## 9 Alangium chinense 1 443 3 0.19
## bio12_score bio12_position climaticMoistureIndex_score
## 3 2 0.29 2
## 5 3 0.09 2
## 6 2 0.14 2
## 7 3 0.21 3
## 8 3 0.09 3
## 9 2 -0.48 1
## climaticMoistureIndex_position monthCountByTemp10_score
## 3 0.21 3
## 5 0.19 3
## 6 0.26 3
## 7 0.20 3
## 8 0.14 3
## 9 -0.77 3
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## 3 NaN 3 0.22
## 5 NaN 1 -0.60
## 6 NaN 1 -0.63
## 7 NaN 2 -0.28
## 8 NaN 1 -0.50
## 9 1 3 0.19
## bio05_score bio05_position bio06_score bio06_position bio16_score
## 3 3 0.07 1 0.54 2
## 5 1 -0.52 2 -0.32 3
## 6 1 -0.64 3 -0.26 2
## 7 3 -0.06 3 -0.05 3
## 8 1 -0.50 3 -0.08 3
## 9 3 -0.12 2 0.52 2
## bio16_position bio17_score bio17_position MCWD_score MCWD_position
## 3 0.44 3 0.10 2 0.38
## 5 0.02 1 0.16 2 0.62
## 6 0.09 2 0.15 2 0.65
## 7 0.27 2 0.80 1 0.88
## 8 -0.07 2 0.40 1 0.55
## 9 -0.33 3 -0.25 2 -0.48summary(treegoer.scores[treegoer.scores$climate.score == 0, ])## species climate.score n.TreeGOER bio01_score
## Length:138 Min. :0 Min. : 1.0 Min. :0.0000
## Class :character 1st Qu.:0 1st Qu.: 22.0 1st Qu.:0.0000
## Mode :character Median :0 Median : 53.5 Median :0.0000
## Mean :0 Mean : 301.1 Mean :0.3333
## 3rd Qu.:0 3rd Qu.: 320.8 3rd Qu.:0.0000
## Max. :0 Max. :3499.0 Max. :3.0000
##
## bio01_position bio12_score bio12_position climaticMoistureIndex_score
## Min. : -Inf Min. :0.00 Min. : -Inf Min. :0.000
## 1st Qu.:-1.952 1st Qu.:1.00 1st Qu.:-0.255 1st Qu.:1.000
## Median :-1.345 Median :2.00 Median :-0.040 Median :2.000
## Mean : -Inf Mean :1.87 Mean : -Inf Mean :1.862
## 3rd Qu.:-1.022 3rd Qu.:3.00 3rd Qu.: 0.700 3rd Qu.:3.000
## Max. : 0.920 Max. :3.00 Max. :27.470 Max. :3.000
##
## climaticMoistureIndex_position monthCountByTemp10_score
## Min. : -Inf Min. :3
## 1st Qu.:-0.1875 1st Qu.:3
## Median : 0.1000 Median :3
## Mean : -Inf Mean :3
## 3rd Qu.: 0.7700 3rd Qu.:3
## Max. :35.0000 Max. :3
##
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## Min. : NA Min. :0.0000 Min. : -Inf
## 1st Qu.: NA 1st Qu.:0.0000 1st Qu.:-1.948
## Median : NA Median :0.0000 Median :-1.345
## Mean :NaN Mean :0.3333 Mean : -Inf
## 3rd Qu.: NA 3rd Qu.:0.0000 3rd Qu.:-1.020
## Max. : NA Max. :3.0000 Max. : 0.920
## NA's :138
## bio05_score bio05_position bio06_score bio06_position
## Min. :0.0000 Min. : -Inf Min. :0.000 Min. : -Inf
## 1st Qu.:0.0000 1st Qu.:-1.430 1st Qu.:0.000 1st Qu.:-1.337
## Median :0.0000 Median :-1.025 Median :1.000 Median :-0.840
## Mean :0.6739 Mean : -Inf Mean :1.007 Mean : -Inf
## 3rd Qu.:1.0000 3rd Qu.:-0.775 3rd Qu.:2.000 3rd Qu.:-0.490
## Max. :3.0000 Max. : 1.240 Max. :3.000 Max. : 1.480
##
## bio16_score bio16_position bio17_score bio17_position
## Min. :0.000 Min. : -Inf Min. :0.000 Min. : -Inf
## 1st Qu.:2.000 1st Qu.:-0.2175 1st Qu.:1.000 1st Qu.: 0.0125
## Median :2.000 Median : 0.0200 Median :2.000 Median : 0.2650
## Mean :2.123 Mean : -Inf Mean :1.862 Mean : -Inf
## 3rd Qu.:3.000 3rd Qu.: 0.4900 3rd Qu.:3.000 3rd Qu.: 0.5650
## Max. :3.000 Max. :24.2900 Max. :3.000 Max. :33.0000
##
## MCWD_score MCWD_position
## Min. :0.000 Min. :-0.750
## 1st Qu.:0.000 1st Qu.: 0.310
## Median :1.000 Median : 0.750
## Mean :1.261 Mean : Inf
## 3rd Qu.:2.000 3rd Qu.: 1.347
## Max. :3.000 Max. : Inf
## head(treegoer.scores[treegoer.scores$climate.score == 0, ])## species climate.score n.TreeGOER bio01_score bio01_position
## 10 Alangium salviifolium 0 48 0 -2.33
## 15 Albizia glaberrima 0 365 0 -1.29
## 19 Albizia zygia 0 1198 0 -1.34
## 25 Antiaris toxicaria 0 1150 0 -1.43
## 28 Asteranthe asterias 0 73 0 -1.51
## 32 Balanites aegyptiaca 0 3499 0 -1.24
## bio12_score bio12_position climaticMoistureIndex_score
## 10 2 -0.43 3
## 15 3 -0.15 3
## 19 2 -0.26 3
## 25 2 -0.22 3
## 28 2 -0.45 2
## 32 2 0.70 1
## climaticMoistureIndex_position monthCountByTemp10_score
## 10 -0.41 3
## 15 0.01 3
## 19 -0.08 3
## 25 -0.08 3
## 28 -0.38 3
## 32 0.89 3
## monthCountByTemp10_position growingDegDays5_score growingDegDays5_position
## 10 NaN 0 -2.36
## 15 NaN 0 -1.28
## 19 NaN 0 -1.34
## 25 NaN 0 -1.43
## 28 NaN 0 -1.50
## 32 NaN 0 -1.23
## bio05_score bio05_position bio06_score bio06_position bio16_score
## 10 0 -1.33 2 -0.80 2
## 15 0 -1.12 1 -0.86 3
## 19 0 -1.23 1 -0.85 3
## 25 0 -1.08 1 -0.85 3
## 28 0 -1.08 0 -1.44 3
## 32 0 -1.10 1 -0.45 2
## bio16_position bio17_score bio17_position MCWD_score MCWD_position
## 10 -0.54 3 0.07 2 0.31
## 15 0.05 2 0.17 3 -0.33
## 19 -0.16 2 0.11 3 0.24
## 25 -0.08 2 0.05 3 0.08
## 28 -0.14 3 -0.34 3 0.29
## 32 0.28 0 1.47 1 0.76Let’s cross-check the calculations for some species
The mean annual temperature for the planting site is:
PL <- as.numeric(site.planting["bio01"])
PL## [1] 18.93For this species encountered with overall score of 3, the parameters are:
species.focal <- treegoer.scores[treegoer.scores$climate.score == 3, ][1, "species"]
species.focal## [1] "Aloe volkensii"species.median <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MEDIAN"])
species.median## [1] 20.45species.minimum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MIN"])
species.minimum## [1] 14.31species.maximum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MAX"])
species.maximum## [1] 26.37This results in the following calculations:
position.if.above.median <- as.numeric((PL - species.median) / (species.maximum - species.median))
position.if.above.median## [1] -0.2567568position.if.below.median <- as.numeric((PL - species.median) / (species.median - species.minimum))
position.if.below.median## [1] -0.247557PL >= species.median## [1] FALSEspecies.position <- ifelse(PL >= species.median,
position.if.above.median,
position.if.below.median)
as.numeric(species.position)## [1] -0.247557as.numeric(round(species.position,
digits=2))## [1] -0.25For another species encountered with overall score of 3, the parameters are:
species.focal <- treegoer.scores[treegoer.scores$climate.score == 3, ][8, "species"]
species.focal## [1] "Erythrococca fischeri"species.median <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MEDIAN"])
species.median## [1] 17.61species.minimum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MIN"])
species.minimum## [1] 14.39species.maximum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MAX"])
species.maximum## [1] 25.28This results in the following calculations:
position.if.above.median <- as.numeric((PL - species.median) / (species.maximum - species.median))
position.if.above.median## [1] 0.1720991position.if.below.median <- as.numeric((PL - species.median) / (species.median - species.minimum))
position.if.below.median## [1] 0.4099379PL >= species.median## [1] TRUEspecies.position <- ifelse(PL >= species.median,
position.if.above.median,
position.if.below.median)
as.numeric(species.position)## [1] 0.1720991as.numeric(round(species.position,
digits=2))## [1] 0.17For a species encountered with overall score of 0, the parameters are:
species.focal <- treegoer.scores[treegoer.scores$climate.score == 0, ][1, "species"]
species.focal## [1] "Alangium salviifolium"species.median <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MEDIAN"])
species.median## [1] 25.32species.minimum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MIN"])
species.minimum## [1] 22.58species.maximum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MAX"])
species.maximum## [1] 28.42This results in the following calculations:
position.if.above.median <- as.numeric((PL - species.median) / (species.maximum - species.median))
position.if.above.median## [1] -2.06129position.if.below.median <- as.numeric((PL - species.median) / (species.median - species.minimum))
position.if.below.median## [1] -2.332117PL >= species.median## [1] FALSEspecies.position <- ifelse(PL >= species.median,
position.if.above.median,
position.if.below.median)
as.numeric(species.position)## [1] -2.332117as.numeric(round(species.position,
digits=2))## [1] -2.33For a species encountered with a bio01_score of 1, the parameters are:
species.focal <- treegoer.scores[treegoer.scores$bio01_score == 1, ][1, "species"]
species.focal## [1] "Adansonia digitata"species.median <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MEDIAN"])
species.median## [1] 26.85species.minimum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MIN"])
species.minimum## [1] 13.68species.maximum <- as.numeric(treegoer.wide[treegoer.wide$species == species.focal, "bio01_MAX"])
species.maximum## [1] 30.53This results in the following calculations:
position.if.above.median <- as.numeric((PL - species.median) / (species.maximum - species.median))
position.if.above.median## [1] -2.152174position.if.below.median <- as.numeric((PL - species.median) / (species.median - species.minimum))
position.if.below.median## [1] -0.6013667PL >= species.median## [1] FALSEspecies.position <- ifelse(PL >= species.median,
position.if.above.median,
position.if.below.median)
as.numeric(species.position)## [1] -0.6013667as.numeric(round(species.position,
digits=2))## [1] -0.6This publication was initiated partially from ongoing work in a Darwin Initiative project (DAREX001) that develops a Global Biodiversity Standard for tree planting. The GlobalUsefulNativeTrees database and Tree Globally Observed Environmental Ranges database were realised through co-funding from this project. With scripts such as the ones shown here, when the Global Biodiversity Standard scheme becomes operational, tree planting projects can seek guidance on suitable species for planting.
sessionInfo()## R version 4.2.1 (2022-06-23 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United Kingdom.utf8
## [2] LC_CTYPE=English_United Kingdom.utf8
## [3] LC_MONETARY=English_United Kingdom.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United Kingdom.utf8
##
## attached base packages:
## [1] tcltk stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BiodiversityR_2.16-1 vegan_2.6-4 lattice_0.20-45
## [4] permute_0.9-7 dplyr_1.1.2 readxl_1.4.1
## [7] data.table_1.14.2
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-157 insight_0.18.4 RColorBrewer_1.1-3
## [4] tools_4.2.1 backports_1.4.1 bslib_0.4.0
## [7] utf8_1.2.2 R6_2.5.1 rpart_4.1.16
## [10] DBI_1.1.3 Hmisc_4.7-1 nortest_1.0-4
## [13] mgcv_1.8-40 colorspace_2.0-3 nnet_7.3-17
## [16] tidyselect_1.2.0 gridExtra_2.3 compiler_4.2.1
## [19] cli_3.4.1 htmlTable_2.4.1 sandwich_3.0-2
## [22] tcltk2_1.2-11 effects_4.2-2 sass_0.4.2
## [25] scales_1.2.1 checkmate_2.1.0 proxy_0.4-27
## [28] RcmdrMisc_2.9-1 stringr_1.4.1 digest_0.6.29
## [31] relimp_1.0-5 foreign_0.8-82 minqa_1.2.4
## [34] rmarkdown_2.16 base64enc_0.1-3 jpeg_0.1-9
## [37] pkgconfig_2.0.3 htmltools_0.5.6 Rcmdr_2.9-1
## [40] lme4_1.1-30 fastmap_1.1.1 htmlwidgets_1.5.4
## [43] rlang_1.1.1 rstudioapi_0.14 jquerylib_0.1.4
## [46] generics_0.1.3 zoo_1.8-11 jsonlite_1.8.0
## [49] car_3.1-0 magrittr_2.0.3 Formula_1.2-4
## [52] interp_1.1-3 Matrix_1.5-1 Rcpp_1.0.11
## [55] munsell_0.5.0 fansi_1.0.3 abind_1.4-5
## [58] lifecycle_1.0.3 stringi_1.7.8 yaml_2.3.5
## [61] carData_3.0-5 MASS_7.3-57 grid_4.2.1
## [64] parallel_4.2.1 forcats_0.5.2 deldir_1.0-6
## [67] haven_2.5.1 splines_4.2.1 hms_1.1.2
## [70] knitr_1.40 pillar_1.9.0 boot_1.3-28
## [73] glue_1.6.2 evaluate_0.16 mitools_2.4
## [76] latticeExtra_0.6-30 png_0.1-7 vctrs_0.6.3
## [79] nloptr_2.0.3 cellranger_1.1.0 gtable_0.3.1
## [82] cachem_1.0.6 ggplot2_3.4.4 xfun_0.33
## [85] survey_4.1-1 e1071_1.7-11 class_7.3-20
## [88] survival_3.3-1 tibble_3.2.1 cluster_2.1.3
## [91] ellipsis_0.3.2