Project #31 - Data Extraction
Timestamp: Tue Nov 3 17:35:46 2020
Drafted: Francesco Maria Sabatini
Version: 1.0
This report documents the data extraction for sPlot project proposal #31 - The adaptive value of xylem physiology within and across global ecoregions as requested by Daniel Laughlin and Jesse Robert Fleri
library(tidyverse)
library(knitr)
library(kableExtra)
library(viridis)
library(grid)
library(gridExtra)
#library(vegan)
#library(xlsx)
#library(caret)
#library(foreign)
#library(raster)
library(downloader)
library(sp)
library(sf)
library(rgdal)
library(rgeos)
#save temporary files
write("TMPDIR = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('TMPDIR'), '.Renviron'))
write("R_USER = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('R_USER'), '.Renviron'))
#rasterOptions(tmpdir="/data/sPlot/users/Francesco/_tmp")Import data from sPlot 3.0
#Import sPlot data
load("/data/sPlot/releases/sPlot3.0/header_sPlot3.0.RData")
load("/data/sPlot/releases/sPlot3.0/DT_sPlot3.0.RData")
load("/data/sPlot/releases/sPlot3.0/Traits_CWMs_sPlot3.RData")
load("/data/sPlot/releases/sPlot3.0/SoilClim_sPlot3.RData")Import data on xylem traits, provided by Jesse Robert Fleri on October 26th, 2020.
load("xylem_data.RData")1 Extract plots from sPlot based on species with xylem traits
Extract all plots containing at least one species in the xylem list.
species_list <- xylem_data$Species
plot.sel <- DT2 %>%
filter(DT2$species %in% species_list) %>%
dplyr::select(PlotObservationID) %>%
distinct() %>%
pull(PlotObservationID)
#exclude plots without geographic information
header.xylem <- header %>%
filter(PlotObservationID %in% plot.sel) %>%
filter(!is.na(Latitude))
#refine plot.sel
plot.sel <- header.xylem$PlotObservationID
DT.xylem <- DT2 %>%
filter(taxon_group %in% c("Vascular plant", "Unknown")) %>%
filter(PlotObservationID %in% plot.sel)Out of the 1841 species in the sRoot list, 1306 species are present in sPlot, for a total of 5510382 records, across 1243968 plots.
2 Extract woody species
This is partial selection, as we don’t have information on the growth form of all species in sPlot
#load list of woody species, as provided to me by Alexander Zizka, within sPlot project #21
load("../Project_21/_input/evowood_species_list.rda")
#Select all woody species and extract relevant traits from TRY
woody_species_traits <- sPlot.traits %>%
dplyr::select(species, GrowthForm, is.tree.or.tall.shrub, n,
starts_with("StemDens"),
starts_with("Stem.cond.dens"),
starts_with("StemConduitDiameter"),
starts_with("LDCM"),
starts_with("SLA"),
starts_with("PlantHeight"),
starts_with("Wood"),
starts_with("SpecificRootLength_mean")) %>%
filter( (species %in% species_list) |
(species %in% synonyms$name_binomial) |
grepl(pattern = "tree|shrub", x = GrowthForm) |
is.tree.or.tall.shrub==T
) %>%
#counter proof - exclude species NOT herb
filter(GrowthForm != "herb" | is.na(GrowthForm))
table(woody_species_traits$GrowthForm, exclude=NULL)##
## herb/shrub herb\\shrub herb/shrub/tree other shrub
## 40 8 2 104 7928
## shrub/tree shrub\\tree tree <NA>
## 105 29 13555 974# MEMO: some standardization needed in sPlot 3.0
#
# Using data from A.Zizka whhen selecting species
# improves the selection only marginally (from ~21k to ~22k)| species | GrowthForm | is.tree.or.tall.shrub | n | StemDens_mean | StemDens_sd | Stem.cond.dens_mean | Stem.cond.dens_sd | StemConduitDiameter_mean | StemConduitDiameter_sd | SLA_mean | SLA_sd | PlantHeight_mean | PlantHeight_sd | Wood.vessel.length_mean | WoodFiberLength_mean | Wood.vessel.length_sd | WoodFiberLength_sd | SpecificRootLength_mean |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lomatia arborescens | shrub/tree | TRUE | 1 | 0.6451425 | NA | 49.221294 | NA | 13.33087 | NA | 7.401691 | NA | 6.1951879 | NA | 446.4706 | 951.3365 | NA | NA | 3560.3223 |
| Lippia salsa | shrub | FALSE | 2 | 0.4572493 | 0.0039409 | 37.063034 | 0.1492967 | 15.40915 | 0.0472494 | 14.072125 | 1.1469204 | 0.4054587 | 0.0139486 | 270.4248 | 852.4892 | 9.136197 | 24.633775 | 5616.0419 |
| Diospyros virginiana | tree | TRUE | 149 | 0.6734528 | 0.0187354 | 15.862129 | 2.8517296 | 102.95699 | 7.8154457 | 23.761360 | 4.4461073 | 16.2110327 | 1.6440175 | 391.1846 | 1140.1778 | 23.216326 | 43.884811 | 3327.8868 |
| Austromyrtus dulcis | shrub | FALSE | 6 | 0.7243857 | 0.0038755 | 48.747695 | 1.6030297 | 23.24232 | 0.7235997 | 8.244685 | 0.1415510 | 6.9188749 | 0.3148530 | 244.3602 | 587.7412 | 4.524264 | 6.633835 | 2595.2214 |
| Tamarix elongata | tree | TRUE | 3 | 0.6143633 | 0.0021182 | 50.380030 | 1.4042630 | 17.21386 | 0.2520982 | 7.837221 | 0.0619457 | 3.9289278 | 0.0540944 | 298.9077 | 537.9980 | 4.500825 | 10.391404 | 3369.3866 |
| Verticordia forrestii | shrub | FALSE | 1 | 0.7642140 | NA | 39.719327 | NA | 29.76079 | NA | 4.195660 | NA | 9.1487785 | NA | 256.1001 | 691.8458 | NA | NA | 2131.0993 |
| Vismia latifolia | tree | TRUE | 3 | 0.4346264 | 0.0067357 | 16.088267 | 0.0245197 | 58.72700 | 2.2047453 | 14.791271 | 0.0630522 | 7.1404928 | 0.2285554 | 305.4112 | 790.9108 | 9.767171 | 17.599158 | 11205.4920 |
| Otoba parvifolia | tree | TRUE | 25 | 0.4316853 | 0.0277815 | 7.633695 | 0.4334815 | 25.52410 | 0.9916954 | 11.153725 | 1.0988266 | 29.5658711 | 2.8799661 | 343.0778 | 1134.3688 | 11.450767 | 27.931322 | 1031.1335 |
| Marsdenia verrucosa | shrub | FALSE | 1 | 0.5874495 | NA | 24.225719 | NA | 80.09720 | NA | 10.303991 | NA | 5.8325524 | NA | 364.5006 | 742.1301 | NA | NA | 2427.5038 |
| Enterolobium barnebianum | tree | TRUE | 2 | 0.5658304 | 0.0063182 | 4.917899 | 0.2569556 | 33.23605 | 1.9370954 | 11.502964 | 0.0397907 | 16.4097715 | 4.3843928 | 255.1133 | 831.3482 | 6.826579 | 35.890797 | 948.8853 |
| Eurya handel-mazzettii | tree | TRUE | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Eschweilera pseudodecolorans | tree | TRUE | 1 | 0.7392958 | NA | 8.634597 | NA | 92.15079 | NA | 10.644061 | NA | 16.3500118 | NA | 494.6545 | 1484.3396 | NA | NA | 662.5836 |
| Roucheria laxiflora | tree | TRUE | 1 | 0.7607442 | NA | 20.686809 | NA | 49.86439 | NA | 9.567348 | NA | 3.3992403 | NA | 484.8660 | 1452.8420 | NA | NA | 3485.5110 |
| Dillwynia tenuifolia | shrub | FALSE | 1 | 0.6983768 | NA | 30.587250 | NA | 27.02294 | NA | 6.591009 | NA | 1.3447485 | NA | 228.4055 | 609.8847 | NA | NA | 1329.1705 |
| Quercus obtusata | tree | TRUE | 2 | 0.7445006 | 0.1006211 | 38.027957 | 0.1531836 | 49.50837 | 0.3036159 | 10.510487 | 0.2590222 | 12.3970918 | 0.4961814 | 330.2405 | 906.1078 | 1.439888 | 27.912641 | 1796.3838 |
| Eremophila fraseri | shrub | FALSE | 2 | 0.8073085 | 0.1115668 | 48.523792 | 0.5818303 | 56.15730 | 2.8009475 | 5.550552 | 0.1917255 | 3.0663162 | 0.1295405 | 639.9068 | 1598.4164 | 23.758321 | 25.766975 | 1665.2501 |
| Iberis sempervirens | shrub | FALSE | 7 | 0.3944325 | 0.0027100 | 87.504174 | 2.9056063 | 25.69286 | 0.2236782 | 23.710124 | 0.1417998 | 0.2210647 | 0.0663102 | 192.4797 | 501.3084 | 2.193399 | 9.501746 | 15361.7573 |
| Archidendron rufescens | tree | TRUE | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Dillenia indica | tree | TRUE | 8 | 0.5936544 | 0.0556893 | 21.933328 | 0.3882042 | 37.68943 | 1.9027573 | 12.387934 | 0.8965556 | 26.0851561 | 1.5404599 | 255.5344 | 462.2107 | 6.162338 | 13.690789 | 1042.9387 |
| Euplassa | shrub | NA | 13 | 0.5104767 | 0.0539766 | 27.544875 | 3.5256873 | 19.47184 | 1.0986517 | 14.602585 | 3.5753732 | 11.0497381 | 1.4307232 | 403.0059 | 974.9209 | 31.366863 | 43.050829 | 3748.3931 |
Selected traits are:
- StemDens - 4 - Stem specific density (SSD) or wood density (stem dry mass per stem fresh volume) (g/cm^3)
- SLA - 11 - Leaf area per leaf dry mass (specific leaf area, SLA or 1/LMA)
- PlantHeight - 18 - Plant height (vegetative + generative)
- StemDiam - 21 - Stem diameter (m)
- Stem.cond.dens - 169 - Stem conduit density (vessels and tracheids) (mm^-2)
- StemConduitDiameter - 281 - Stem conduit diameter (vessels, tracheids)_micro (m)
- Wood.vessel.length - 282 - Wood vessel element length; stem conduit (vessel and tracheids) element length_micro (m)
- WoodFiberLength - 289 - Wood fiber lengths_micro (m)
- SpecificRootLength - 1080 - Root length per root dry mass (specific root length, SRL) (cm/g)
Codes correspond to those reported in TRY
#subset DT.xylem to only retain woody species
DT.xylem <- DT.xylem %>%
filter(species %in% (woody_species_traits$species))
nrow(DT.xylem)## [1] 8567900Merge relative cover across vegetation layers, if needed, and normalize to 1 (=100%)
###combine cover values across layers
combine.cover <- function(x){
while (length(x)>1){
x[2] <- x[1]+(100-x[1])*x[2]/100
x <- x[-1]
}
return(x)
}
DT.xylem <- DT.xylem %>%
dplyr::select(PlotObservationID, species,Layer, Relative.cover) %>%
# normalize relative cover to 100
left_join({.} %>%
group_by(PlotObservationID, Layer) %>%
summarize(Tot.Cover=sum(Relative.cover), .groups="drop"),
by=c("PlotObservationID", "Layer")) %>%
mutate(Relative.cover=Relative.cover/Tot.Cover) %>%
group_by(PlotObservationID, species) %>%
summarize(Relative.cover=combine.cover(Relative.cover), .groups="drop") %>%
ungroup()
nrow(DT.xylem)## [1] 73240903 Calculate CWMs and trait coverage
Calculate CWM and trait coverage for each trait and each plot. Select plots having more than 80% coverage for at least one trait.
# Merge species data table with traits
CWM.xylem0 <- DT.xylem %>%
as_tibble() %>%
dplyr::select(PlotObservationID, species, Relative.cover) %>%
left_join(xylem_data %>%
dplyr::rename(species=Species) %>%
dplyr::select(species, P50, Ks),
by="species")
# Calculate CWM for each trait in each plot
CWM.xylem1 <- CWM.xylem0 %>%
group_by(PlotObservationID) %>%
summarize_at(.vars= vars(P50:Ks),
.funs = list(~weighted.mean(., Relative.cover, na.rm=T)),
.groups="drop") %>%
dplyr::select(PlotObservationID, order(colnames(.))) %>%
pivot_longer(-PlotObservationID, names_to="trait", values_to="trait.value")
# Calculate coverage for each trait in each plot
CWM.xylem2 <- CWM.xylem0 %>%
mutate_at(.funs = list(~if_else(is.na(.),0,1) * Relative.cover),
.vars = vars(P50:Ks)) %>%
group_by(PlotObservationID) %>%
summarize_at(.vars= vars(P50:Ks),
.funs = list(~sum(., na.rm=T)),
.groups="drop") %>%
dplyr::select(PlotObservationID, order(colnames(.))) %>%
pivot_longer(-PlotObservationID, names_to="trait", values_to="trait.coverage")
# Calculate CWV
variance2.fun <- function(trait, abu){
res <- as.double(NA)
#nam <- nam[!is.na(trait)]
abu <- abu[!is.na(trait)]
trait <- trait[!is.na(trait)]
abu <- abu/sum(abu)
if (length(trait)>1){
# you need more than 1 observation to calculate
# skewness and kurtosis
# for calculation see
# http://r.789695.n4.nabble.com/Weighted-skewness-and-curtosis-td4709956.html
m.trait <- weighted.mean(trait,abu)
res <- sum(abu*(trait-m.trait)^2)
}
res
}
CWM.xylem3 <- CWM.xylem0 %>%
group_by(PlotObservationID) %>%
summarize_at(.vars= vars(P50:Ks),
.funs = list(~variance2.fun(., Relative.cover))) %>%
dplyr::select(PlotObservationID, order(colnames(.))) %>%
pivot_longer(-PlotObservationID, names_to="trait", values_to="trait.variance")
## Calculate proportion of species having traits
CWM.xylem4 <- CWM.xylem0 %>%
group_by(PlotObservationID) %>%
summarize_at(.vars= vars(P50:Ks),
.funs = list(~sum(!is.na(.)))) %>%
dplyr::select(PlotObservationID, order(colnames(.))) %>%
pivot_longer(-PlotObservationID, names_to="trait", values_to="trait.nspecies")
# Join together
CWM.xylem <- CWM.xylem1 %>%
left_join(CWM.xylem2, by=c("PlotObservationID", "trait")) %>%
left_join(CWM.xylem3, by=c("PlotObservationID", "trait")) %>%
left_join(CWM.xylem4, by=c("PlotObservationID", "trait")) %>%
left_join(CWM.xylem0 %>%
group_by(PlotObservationID) %>%
summarize(sp.richness=n()), by=c("PlotObservationID"),
.groups="drop") %>%
mutate(trait.coverage.nspecies=trait.nspecies/sp.richness) %>%
#filter(trait.coverage>=0.8) %>%
arrange(PlotObservationID)## `summarise()` ungrouping output (override with `.groups` argument)| PlotObservationID | trait | trait.value | trait.coverage | trait.variance | trait.nspecies | sp.richness | trait.coverage.nspecies |
|---|---|---|---|---|---|---|---|
| 5 | Ks | 1.6950265 | 0.2857143 | NA | 1 | 4 | 0.2500000 |
| 5 | P50 | -1.5003056 | 0.2857143 | NA | 1 | 4 | 0.2500000 |
| 16 | Ks | 0.1557417 | 0.9677419 | 0.0014506 | 2 | 7 | 0.2857143 |
| 16 | P50 | -2.3015323 | 1.0000000 | 0.5938076 | 3 | 7 | 0.4285714 |
| 17 | Ks | 0.3915309 | 1.9138182 | 0.1519621 | 5 | 7 | 0.7142857 |
| 17 | P50 | -3.2692863 | 1.9138182 | 3.5600373 | 5 | 7 | 0.7142857 |
| 18 | Ks | 0.4580143 | 0.7444444 | 0.0410046 | 4 | 10 | 0.4000000 |
| 18 | P50 | -3.8911671 | 3.3333333 | 5.0350743 | 8 | 10 | 0.8000000 |
| 19 | Ks | 0.7376319 | 1.0057455 | 0.1460626 | 5 | 14 | 0.3571429 |
| 19 | P50 | -3.3159887 | 1.9539273 | 1.2686962 | 9 | 14 | 0.6428571 |
| 20 | Ks | 0.3050261 | 0.0526316 | 0.0160221 | 2 | 7 | 0.2857143 |
| 20 | P50 | -2.7121667 | 0.5526316 | 1.0054947 | 3 | 7 | 0.4285714 |
| 21 | Ks | 0.3050261 | 1.0000000 | 0.0160221 | 2 | 8 | 0.2500000 |
| 21 | P50 | -3.8529856 | 1.7755102 | 4.3799595 | 3 | 8 | 0.3750000 |
| 22 | Ks | NaN | 0.0000000 | NA | 0 | 2 | 0.0000000 |
| 22 | P50 | NaN | 0.0000000 | NA | 0 | 2 | 0.0000000 |
| 26 | Ks | 0.4665221 | 0.8214286 | 0.0210918 | 2 | 8 | 0.2500000 |
| 26 | P50 | -3.8980845 | 1.1153846 | 1.2427961 | 5 | 8 | 0.6250000 |
| 27 | Ks | 0.1500000 | 0.0315789 | NA | 1 | 7 | 0.1428571 |
| 27 | P50 | -4.0094507 | 0.0526316 | 5.2985022 | 2 | 7 | 0.2857143 |
CWM.xylem08 <- CWM.xylem %>%
filter(trait.coverage>=0.8)## `summarise()` ungrouping output (override with `.groups` argument)| trait | num.plots | .group |
|---|---|---|
| Ks | 314553 | drop |
| P50 | 529224 | drop |
#Create list of plots having at least one trait with >=.8 coverage and extract header data
#plot80perc <- (CWM.xylem %>%
# dplyr::select(PlotObservationID) %>%
# distinct())$PlotObservationID
#DT.xylem08 <- DT.xylem %>%
# filter(PlotObservationID %in% header.xylem$PlotID)
#CWM.xylem <- CWM.xylem %>%
# filter(PlotObservationID %in% header.xylem$PlotID) | Completeness_perc | |
|---|---|
| GIVD ID | 99.9944532 |
| TV2 relevé number | 100.0000000 |
| ORIG_NUM | 0.0047429 |
| GUID | 100.0000000 |
| Longitude | 100.0000000 |
| Latitude | 100.0000000 |
| Location uncertainty (m) | 95.2326748 |
| Country | 99.9040972 |
| CONTINENT | 100.0000000 |
| sBiome | 99.2994997 |
| sBiomeID | 99.2994997 |
| Ecoregion | 99.2824574 |
| EcoregionID | 99.2824574 |
| Locality | 60.0678635 |
| Relevé area (m²) | 72.9285641 |
| Cover abundance scale | 100.0000000 |
| Date of recording | 87.1815834 |
| Plants recorded | 99.9995177 |
| Herbs identified (y/n) | 2.7118865 |
| Mosses identified (y/n) | 28.1309487 |
| Lichens identified (y/n) | 16.5480141 |
| elevation_dem | 76.7620228 |
| Altitude (m) | 84.2275686 |
| Aspect (°) | 32.4671535 |
| Slope (°) | 42.1329970 |
| Forest | 74.7368903 |
| Shrubland | 74.7368903 |
| Grassland | 74.7368903 |
| Wetland | 74.7368903 |
| Sparse.vegetation | 74.7368903 |
| Naturalness | 47.8299281 |
| ESY | 72.6619977 |
| Cover total (%) | 21.1678275 |
| Cover tree layer (%) | 17.0949735 |
| Cover shrub layer (%) | 18.6166364 |
| Cover herb layer (%) | 36.1539847 |
| Cover moss layer (%) | 18.1638113 |
| Cover lichen layer (%) | 0.3121463 |
| Cover algae layer (%) | 0.0567539 |
| Cover litter layer (%) | 4.5210166 |
| Cover open water (%) | 0.1392319 |
| Cover bare rock (%) | 1.7043043 |
| Height (highest) trees (m) | 7.5995524 |
| Height lowest trees (m) | 0.4962346 |
| Height (highest) shrubs (m) | 5.0405637 |
| Height lowest shrubs (m) | 0.5556413 |
| Aver. height (high) herbs (cm) | 9.5914847 |
| Aver. height lowest herbs (cm) | 2.7044104 |
| Maximum height herbs (cm) | 2.4628447 |
| Maximum height cryptogams (mm) | 0.1219485 |
The process results in 1243968 plots selected, for a total of 1932998 trait * plot combinations.
Geographical distribution of plots
countries <- map_data("world")
ggworld <- ggplot(countries, aes(x=long, y=lat, group = group)) +
geom_polygon(col=NA, lwd=3, fill = gray(0.9)) +
geom_point(data=header.xylem, aes(x=Longitude, y=Latitude, group=1), col="red", alpha=0.5, cex=0.7, shape="+") +
theme_bw()
ggworld
Summarize data across data sets in sPlot, and create list of data custodians
db.out <- read_csv("/data/sPlot/users/Francesco/_sPlot_Management/Consortium/Databases.out.csv") %>%
dplyr::select(`GIVD ID`, Custodian)
data.origin <- header.xylem %>%
group_by(`GIVD ID`) %>%
summarize(Num.plot=n(), .groups="drop") %>%
left_join(db.out)| GIVD ID | Num.plot | Custodian |
|---|---|---|
| 00-00-001 | 1634 | Oliver L. Phillips |
| 00-00-003 | 2318 | Brian Enquist |
| 00-00-004 | 297 | Risto Virtanen |
| 00-00-005 | 115 | Anne D. Bjorkman |
| 00-RU-001 | 1100 | Vassiliy Martynenko |
| 00-RU-002 | 1044 | Milan Chytrý |
| 00-RU-006 | 1079 | Sergey Yamalov |
| 00-RU-XXX | 186 | Olga Demina |
| 00-TR-001 | 6653 | Ali Kavgacı |
| 00-TR-002 | 962 | Deniz Işık Gürsoy |
| AF-00-001 | 80 | Marco Schmidt |
| AF-00-003 | 80 | Norbert Jürgens |
| AF-00-003 | 80 | Norbert Jürgens |
| AF-00-006 | 1070 | Miguel Alvarez |
| AF-00-008 | 207 | Hjalmar Kühl |
| AF-00-009 | 189 | Rasmus Revermann |
| AF-00-010 | 16 | Petr Sklenar |
| AF-00-011 | 93 | Bruno Herault |
| AF-CD-001 | 18 | Kim Sarah Jacobsen |
| AF-CM-001 | 57 | Jiri Dolezal |
| AF-EG-XXX | 1596 | Mohamed Abd El-Rouf Mousa El-Sheikh |
| AF-ET-001 | 36 | Desalegn Wana |
| AF-MA-001 | 203 | Manfred Finckh |
| AF-NA-001 | 9 | Ben Strohbach |
| AF-ZA-003 | 1736 | John Janssen |
| AF-ZW-001 | 20 | Cyrus Samimi |
| AS-00-001 | 4536 | Tomáš Černý |
| AS-00-003 | 629 | Arkadiusz Nowak |
| AS-BD-001 | 150 | Mohammed A.S. Arfin Khan |
| AS-CN-001 | 84 | Hongyan Liu |
| AS-CN-002 | 32 | Karsten Wesche |
| AS-CN-003 | 27 | Helge Bruelheide |
| AS-CN-004 | 380 | Zhiyao Tang |
| AS-CN-007 | 843 | Zhiyao Tang |
| AS-CN-008 | 432 | Hua-Feng Wang |
| AS-CN-XXX | 70 | Cindy Q. Tang |
| AS-EG-001 | 3 | Mohamed Z. Hatim |
| AS-ID-001 | 23 | Michael Kessler |
| AS-ID-002 | 142 | Holger Kreft |
| AS-ID-XXX | 809 | Jiri Dolezal |
| AS-IR-001 | 810 | Jalil Noroozi |
| AS-IR-006 | 1193 | Hamid Gholizadeh |
| AS-JP-002 | 46720 | Yasuhiro Kubota |
| AS-KG-001 | 439 | Peter Borchardt |
| AS-KZ-001 | 57 | Viktoria Wagner |
| AS-MN-001 | 147 | Henrik von Wehrden |
| AS-RU-001 | 112 | Victor Chepinoga |
| AS-RU-002 | 3345 | Andrey Korolyuk |
| AS-RU-004 | 158 | Norbert Hölzel |
| AS-RU-005 | 530 | Igor Lavrinenko |
| AS-SA-001 | 17 | Mohamed Abd El-Rouf Mousa El-Sheikh |
| AS-TJ-001 | 15 | Kim André Vanselow |
| AS-TR-002 | 1145 | Emin Uğurlu |
| AS-TW-001 | 885 | Ching-Feng Li |
| AS-YE-001 | 8 | Michele De Sanctis |
| AU-AU-002 | 7797 | Ben Sparrow |
| AU-AU-003 | 12767 | John Thomas Hunter |
| AU-AU-XXX | 4405 | Paul David Macintyre |
| AU-NC-001 | 137 | Jérôme Munzinger |
| AU-NZ-001 | 15738 | Susan Wiser |
| AU-PG-001 | 63 | Timothy Whitfeld |
| EU-00-002 | 3831 | Jürgen Dengler |
| EU-00-004 | 3486 | Xavier Font |
| EU-00-004a | 1606 | Borja Jiménez-Alfaro |
| EU-00-004b | 2401 | Xavier Font |
| EU-00-004c | 5805 | Maria Pilar Rodríguez-Rojo |
| EU-00-004d | 1449 | Borja Jiménez-Alfaro |
| EU-00-004e | 3616 | Federico Fernández-González |
| EU-00-004f | 9303 | Federico Fernández-González |
| EU-00-004g | 922 | Rosario G Gavilán |
| EU-00-011 | 11801 | Idoia Biurrun |
| EU-00-013 | 4540 | Kiril Vassilev |
| EU-00-016 | 493 | Corrado Marcenò |
| EU-00-017 | 244 | John Janssen |
| EU-00-018 | 9097 | Jonathan Lenoir |
| EU-00-019 | 11044 | Kiril Vassilev |
| EU-00-020 | 1319 | Flavia Landucci |
| EU-00-021 | 3714 | Andraž Carni |
| EU-00-022 | 3825 | Tomáš Peterka |
| EU-00-023 | 6251 | Juan Antonio Campos |
| EU-00-024 | 4548 | Idoia Biurrun |
| EU-00-026 | 6764 | Gianmaria Bonari |
| EU-00-027 | 12982 | Anni Pyykönen |
| EU-00-028 | 7983 | Filip Küzmič |
| EU-AL-001 | 237 | Michele De Sanctis |
| EU-AT-001 | 40800 | Wolfgang Willner |
| EU-BE-002 | 18242 | Els De Bie |
| EU-BG-001 | 3935 | Iva Apostolova |
| EU-CH-005 | 14182 | Thomas Wohlgemuth |
| EU-CH-011 | 5010 | Ariel Bergamini |
| EU-CZ-001 | 67465 | Milan Chytrý |
| EU-DE-001 | 26234 | Florian Jansen |
| EU-DE-013 | 19540 | Florian Jansen |
| EU-DE-014 | 44179 | Ute Jandt |
| EU-DE-020 | 4607 | Jürgen Dengler |
| EU-DE-035 | 2997 | Maike Isermann |
| EU-DE-040 | 2119 | Joachim Schrautzer |
| EU-DK-002 | 124884 | Jesper Erenskjold Moeslund |
| EU-ES-001 | 1024 | Aaron Pérez-Haase |
| EU-FR-003 | 132104 | Emmanuel Garbolino |
| EU-FR-005 | 5012 | Jean-Claude Gegout |
| EU-GB-001 | 29058 | John S. Rodwell |
| EU-GB-XXX | 22387 | Irina Tatarenko |
| EU-GR-001 | 2410 | Erwin Bergmeier |
| EU-GR-005 | 4696 | Panayotis Dimopoulos |
| EU-GR-006 | 3182 | Ioannis Tsiripidis |
| EU-HR-001 | 2941 | Zvjezdana Stančić |
| EU-HR-002 | 7306 | Željko Škvorc |
| EU-HU-003 | 4842 | János Csiky |
| EU-IE-001 | 17901 | Úna FitzPatrick |
| EU-IT-001 | 6832 | Roberto Venanzoni |
| EU-IT-010 | 3707 | Laura Casella |
| EU-IT-011 | 19597 | Emiliano Agrillo |
| EU-IT-019 | 552 | Angela Stanisci |
| EU-LT-001 | 6713 | Valerijus Rašomavičius |
| EU-LV-001 | 5179 | Solvita Rūsiņa |
| EU-MK-001 | 791 | Renata Ćušterevska |
| EU-NL-001 | 117910 | Joop H.J. Schaminée |
| EU-PL-001 | 53216 | Zygmunt Kącki |
| EU-PL-003 | 3849 | Remigiusz Pielech |
| EU-RO-007 | 9586 | Adrian Indreica |
| EU-RO-008 | 17914 | Eszter Ruprecht |
| EU-RS-002 | 2417 | Svetlana Aćić |
| EU-RS-003 | 3408 | Mirjana Cuk |
| EU-RU-002 | 1563 | Valentin Golub |
| EU-RU-003 | 204 | Tatiana Lysenko |
| EU-RU-011 | 8147 | Vadim Prokhorov |
| EU-RU-014 | 4824 | Larisa Khanina |
| EU-SI-001 | 15590 | Urban Šilc |
| EU-SK-001 | 29122 | Milan Valachovič |
| EU-UA-001 | 2446 | Anna Kuzemko |
| EU-UA-005 | 598 | Tetiana Dziuba |
| EU-UA-006 | 2079 | Viktor Onyshchenko |
| NA-00-002 | 1294 | Luis Cayuela |
| NA-CA-003 | 38 | Viktoria Wagner |
| NA-CA-004 | 147 | Isabelle Aubin |
| NA-CA-005 | 89 | Yves Bergeron |
| NA-CU-XXX | 207 | Ute Jandt |
| NA-GL-001 | 35 | Birgit Jedrzejek |
| NA-US-002 | 55282 | Robert K. Peet |
| NA-US-006 | 15302 | Robert K. Peet |
| NA-US-008 | 551 | Donald Waller |
| NA-US-014 | 1390 | Donald A. Walker |
| NA-US-016 | 490 | Dylan Craven |
| SA-00-002 | 176 | Gwendolyn Peyre |
| SA-00-003 | 220 | Glenda Mendieta-Leiva |
| SA-AR-002 | 232 | Melisa Giorgis |
| SA-AR-003 | 141 | Karina Speziale |
| SA-BO-003 | 73 | Michael Kessler |
| SA-BR-002 | 1576 | Alexander Christian Vibrans |
| SA-CL-002 | 213 | Alvaro G. Gutierrez |
| SA-CL-003 | 100 | Aníbal Pauchard |
| SA-CO-003 | 200 | Esteban Alvarez-Davila |
| SA-EC-001 | 129 | Jürgen Homeier |
| SA-EC-002 | 6 | Gonzalo Rivas-Torres |
| NA | 69 | NA |
The data derive from 156 datasets.
4 Extract climate and soils data
soilclim.xylem <- soilclim %>%
filter(PlotObservationID %in% plot.sel) %>%
rename(Elevation=Elevation_median, -Elevation_q2.5, -Elevation_q97.5)| PlotObservationID | Elevation | Elevation_q2.5 | Elevation_q97.5 | Elevation_DEM.res | bio01 | bio02 | bio03 | bio04 | bio05 | bio06 | bio07 | bio08 | bio09 | bio10 | bio11 | bio12 | bio13 | bio14 | bio15 | bio16 | bio17 | bio18 | bio19 | bio01sd | bio02sd | bio03sd | bio04sd | bio05sd | bio06sd | bio07sd | bio08sd | bio09sd | bio10sd | bio11sd | bio12sd | bio13sd | bio14sd | bio15sd | bio16sd | bio17sd | bio18sd | bio19sd | BLDFIE | CECSOL | CLYPPT | CRFVOL | ORCDRC | PHIHOX | SLTPPT | SNDPPT | BLDFIEsd | CECSOLsd | CLYPPTsd | CRFVOLsd | ORCDRCsd | PHIHOXsd | SLTPPTsd | SNDPPTsd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1949517 | 2810 | 2810 | 2810 | 153 | 64.00000 | 122.00000 | 379.0000 | 7066.000 | 233.0000 | -89.00000 | 321.0000 | 158.00000 | 81.00000 | 160.0000 | -30.00000 | 598.0000 | 118.00000 | 15.00000 | 60.00000 | 331.0000 | 48.00000 | 208.0000 | 150.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1075.8000 | 29.20000 | 20.800000 | 18.000000 | 68.40000 | 59.80000 | 38.60000 | 40.80000 | 14.41180 | 0.8366600 | 0.4472136 | 0.7071068 | 5.8137767 | 0.8366600 | 0.5477226 | 0.8366600 |
| 666779 | NA | NA | NA | NA | 117.48204 | 74.00000 | 310.0945 | 5769.304 | 251.9301 | 12.57467 | 239.3459 | 144.01323 | 67.82420 | 200.7032 | 43.22306 | 744.4877 | 80.23629 | 50.77883 | 12.75992 | 217.0870 | 156.07940 | 192.6711 | 183.1474 | 2.0310466 | 0.0000000 | 0.3825604 | 15.061490 | 2.2158230 | 1.8866058 | 0.6595651 | 16.9136010 | 2.7201027 | 2.1454694 | 1.8978051 | 62.959014 | 6.8112350 | 4.3447815 | 0.5814532 | 18.325859 | 13.903119 | 16.499240 | 16.215669 | 988.5854 | 16.25147 | 20.636278 | 12.025206 | 23.63887 | 62.96561 | 38.21720 | 41.12073 | 92.95901 | 2.2463988 | 3.4595559 | 1.8352642 | 5.7520812 | 4.4650441 | 3.4865659 | 5.6465916 |
| 839747 | 76 | 76 | 76 | 153 | 98.00000 | 66.00000 | 284.0000 | 5919.000 | 226.0000 | -7.00000 | 233.0000 | 176.00000 | 20.00000 | 181.0000 | 20.00000 | 769.0000 | 81.00000 | 46.00000 | 14.00000 | 235.0000 | 153.00000 | 227.0000 | 153.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 818.5000 | 12.83333 | 10.833333 | 6.000000 | 22.66667 | 62.50000 | 32.33333 | 56.83333 | 24.01458 | 0.9831921 | 0.4082483 | 0.0000000 | 0.8164966 | 1.0488088 | 0.5163978 | 0.4082483 |
| 1167099 | -2 | -2 | -2 | 153 | 103.00000 | 51.00000 | 260.0000 | 5094.000 | 207.0000 | 12.00000 | 195.0000 | 88.00000 | 79.00000 | 176.0000 | 35.00000 | 840.0000 | 86.00000 | 46.00000 | 19.00000 | 256.0000 | 151.00000 | 245.0000 | 175.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 351.8000 | 31.20000 | 24.000000 | 4.800000 | 167.40000 | 59.40000 | 30.20000 | 45.60000 | 22.29798 | 1.0954451 | 1.5811388 | 0.4472136 | 10.1390335 | 0.5477226 | 1.0954451 | 2.3021729 |
| 654053 | 158 | 158 | 158 | 153 | 111.00000 | 74.00000 | 292.0000 | 6370.000 | 250.0000 | -4.00000 | 255.0000 | 172.00000 | 29.00000 | 200.0000 | 27.00000 | 750.0000 | 85.00000 | 44.00000 | 23.00000 | 255.0000 | 133.00000 | 233.0000 | 146.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 852.6000 | 16.60000 | 18.800000 | 10.600000 | 37.60000 | 57.60000 | 38.40000 | 42.80000 | 28.97067 | 0.5477226 | 0.8366600 | 0.5477226 | 1.5165751 | 1.1401754 | 0.5477226 | 0.4472136 |
| 319486 | 583 | 583 | 583 | 153 | 76.00000 | 76.00000 | 265.0000 | 7399.000 | 226.0000 | -58.00000 | 285.0000 | 172.00000 | 11.00000 | 176.0000 | -25.00000 | 723.0000 | 89.00000 | 41.00000 | 25.00000 | 253.0000 | 125.00000 | 226.0000 | 148.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 855.0000 | 21.40000 | 22.000000 | 14.000000 | 42.40000 | 60.40000 | 45.80000 | 32.40000 | 47.72840 | 1.1401754 | 1.0000000 | 0.7071068 | 11.4149025 | 0.5477226 | 0.8366600 | 0.5477226 |
| 34741 | 140 | 140 | 142 | 153 | 107.00000 | 79.00000 | 272.0000 | 7545.000 | 261.0000 | -31.00000 | 292.0000 | 204.66667 | 9.00000 | 208.0000 | 3.00000 | 555.0000 | 69.66667 | 31.33333 | 28.00000 | 200.3333 | 94.66667 | 184.6667 | 100.3333 | 0.0000000 | 0.0000000 | 0.0000000 | 2.000000 | 0.0000000 | 0.0000000 | 0.0000000 | 0.5773503 | 0.0000000 | 0.0000000 | 0.0000000 | 5.000000 | 0.5773503 | 0.5773503 | 0.0000000 | 1.527525 | 1.527525 | 1.527525 | 1.527525 | 1141.7674 | 22.95349 | 22.976744 | 6.511628 | 24.88372 | 68.79070 | 32.95349 | 43.95349 | 51.53078 | 1.7586993 | 1.9938999 | 0.6680495 | 1.8671254 | 1.8068407 | 2.3599182 | 3.9154972 |
| 1661863 | NA | NA | NA | NA | 141.22535 | 86.85312 | 309.7364 | 6645.231 | 294.9718 | 14.55332 | 280.3924 | 117.43461 | 230.11066 | 238.3763 | 56.41650 | 685.6036 | 88.26358 | 30.04829 | 27.46278 | 253.0584 | 104.14889 | 122.6237 | 166.4145 | 7.6047471 | 0.8427557 | 1.3607923 | 32.732432 | 7.7915905 | 7.4146810 | 1.6378161 | 7.4466009 | 7.8633636 | 7.8361034 | 7.3912526 | 74.279427 | 8.7897107 | 4.2985465 | 2.4836406 | 24.973910 | 16.705284 | 18.180360 | 19.988435 | 1224.4096 | 22.73415 | 28.579681 | 14.798945 | 24.71179 | 72.35150 | 40.10536 | 31.33518 | 120.15481 | 1.7616083 | 2.3264205 | 1.8513969 | 7.3508237 | 3.5478420 | 2.0707349 | 3.4727637 |
| 1059148 | 11 | 8 | 15 | 153 | 103.00000 | 65.00000 | 296.6667 | 5446.833 | 223.6667 | 4.00000 | 219.1667 | 49.83333 | 83.00000 | 180.0000 | 31.00000 | 785.6667 | 76.33333 | 46.66667 | 12.33333 | 224.6667 | 155.83333 | 210.8333 | 175.5000 | 0.0000000 | 0.0000000 | 0.5163978 | 2.316607 | 0.5163978 | 0.0000000 | 0.4082483 | 0.4082483 | 0.0000000 | 0.0000000 | 0.0000000 | 5.537749 | 0.5163978 | 0.5163978 | 0.5163978 | 1.032796 | 1.471960 | 1.471960 | 1.643168 | 403.9787 | 11.77660 | 4.904255 | 5.042553 | 29.47872 | 53.64894 | 13.72340 | 81.32979 | 34.10420 | 1.0176322 | 1.5455044 | 0.6544290 | 3.6092138 | 1.3496909 | 1.7314563 | 2.9270275 |
| 1469065 | 134 | 134 | 134 | 153 | 108.00000 | 80.00000 | 272.0000 | 7625.000 | 264.0000 | -32.00000 | 296.0000 | 207.00000 | 9.00000 | 210.0000 | 3.00000 | 592.0000 | 71.00000 | 34.00000 | 25.00000 | 207.0000 | 104.00000 | 194.0000 | 112.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1205.8333 | 25.33333 | 23.000000 | 7.000000 | 25.50000 | 75.16667 | 39.50000 | 37.00000 | 18.92529 | 0.8164966 | 0.6324555 | 0.0000000 | 0.5477226 | 0.4082483 | 0.5477226 | 0.6324555 |
| 715005 | 70 | 70 | 70 | 153 | 152.00000 | 57.00000 | 260.0000 | 5574.000 | 274.0000 | 54.00000 | 219.0000 | 176.00000 | 227.00000 | 235.0000 | 82.00000 | 557.0000 | 90.00000 | 15.00000 | 39.00000 | 226.0000 | 56.00000 | 75.0000 | 158.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1288.1667 | 20.83333 | 27.333333 | 18.000000 | 29.33333 | 72.33333 | 36.00000 | 36.66667 | 24.01180 | 0.4082483 | 0.8164966 | 1.4142136 | 3.4448028 | 0.5163978 | 1.5491933 | 1.2110601 |
| 290933 | 857 | 857 | 857 | 153 | 62.00000 | 75.00000 | 278.0000 | 6903.000 | 204.0000 | -65.00000 | 269.0000 | 152.00000 | 80.00000 | 156.0000 | -30.00000 | 900.0000 | 101.00000 | 56.00000 | 21.00000 | 301.0000 | 180.00000 | 295.0000 | 221.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 674.6667 | 21.50000 | 16.500000 | 14.333333 | 64.00000 | 53.00000 | 39.50000 | 43.83333 | 129.47381 | 1.7606817 | 0.8366600 | 0.8164966 | 7.6157731 | 2.8284271 | 1.3784049 | 2.3166067 |
| 783594 | 73 | 53 | 94 | 153 | 94.76042 | 68.00000 | 271.0000 | 6537.792 | 232.1146 | -17.37500 | 249.2917 | 185.76042 | 9.59375 | 185.7604 | 9.59375 | 536.0312 | 61.28125 | 32.39583 | 21.01042 | 175.9167 | 102.32292 | 175.9167 | 102.3229 | 0.7913319 | 0.0000000 | 0.0000000 | 4.737125 | 0.7659846 | 0.8239826 | 0.4569157 | 0.8302795 | 0.7891118 | 0.8302795 | 0.7891118 | 13.104803 | 1.5471748 | 0.9000487 | 0.5128823 | 4.354469 | 2.815352 | 4.354469 | 2.815352 | 765.0408 | 14.08744 | 5.782380 | 6.330046 | 44.14135 | 51.94543 | 17.73636 | 76.40237 | 102.96033 | 2.1796563 | 2.1224005 | 1.0215819 | 14.1320513 | 5.5395985 | 4.7011532 | 6.2167934 |
| 671367 | NA | NA | NA | NA | 117.48204 | 74.00000 | 310.0945 | 5769.304 | 251.9301 | 12.57467 | 239.3459 | 144.01323 | 67.82420 | 200.7032 | 43.22306 | 744.4877 | 80.23629 | 50.77883 | 12.75992 | 217.0870 | 156.07940 | 192.6711 | 183.1474 | 2.0310466 | 0.0000000 | 0.3825604 | 15.061490 | 2.2158230 | 1.8866058 | 0.6595651 | 16.9136010 | 2.7201027 | 2.1454694 | 1.8978051 | 62.959014 | 6.8112350 | 4.3447815 | 0.5814532 | 18.325859 | 13.903119 | 16.499240 | 16.215669 | 988.5854 | 16.25147 | 20.636278 | 12.025206 | 23.63887 | 62.96561 | 38.21720 | 41.12073 | 92.95901 | 2.2463988 | 3.4595559 | 1.8352642 | 5.7520812 | 4.4650441 | 3.4865659 | 5.6465916 |
| 294468 | 270 | 270 | 271 | 153 | 87.00000 | 74.00000 | 276.0000 | 6987.000 | 231.0000 | -39.00000 | 269.0000 | 182.00000 | -5.00000 | 182.0000 | -6.00000 | 630.0000 | 74.00000 | 36.00000 | 24.00000 | 219.0000 | 113.00000 | 219.0000 | 132.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 722.5000 | 19.50000 | 17.000000 | 14.000000 | 106.50000 | 52.50000 | 36.00000 | 47.00000 | 34.64823 | 2.1213203 | 1.4142136 | 1.4142136 | 12.0208153 | 0.7071068 | 1.4142136 | 1.4142136 |
| 689060 | 1021 | 1021 | 1021 | 153 | 74.00000 | 76.00000 | 313.0000 | 5787.000 | 211.0000 | -33.00000 | 244.0000 | 86.00000 | 23.00000 | 158.0000 | 0.00000 | 1041.0000 | 119.00000 | 65.00000 | 16.00000 | 319.0000 | 203.00000 | 269.0000 | 241.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 529.4000 | 30.20000 | 26.600000 | 20.200000 | 88.20000 | 52.60000 | 33.20000 | 40.00000 | 56.30542 | 0.4472136 | 2.1908902 | 2.1679483 | 6.0580525 | 1.1401754 | 0.8366600 | 1.5811388 |
| 843766 | 64 | 64 | 64 | 153 | 99.00000 | 66.00000 | 291.0000 | 5688.000 | 223.0000 | -3.00000 | 226.0000 | 173.00000 | 79.00000 | 178.0000 | 24.00000 | 808.0000 | 80.00000 | 50.00000 | 13.00000 | 230.0000 | 163.00000 | 224.0000 | 177.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 507.1667 | 14.83333 | 10.333333 | 5.000000 | 46.16667 | 56.50000 | 23.00000 | 66.66667 | 43.65051 | 1.4719601 | 1.8618987 | 0.0000000 | 9.4533945 | 0.8366600 | 1.4142136 | 2.8047579 |
| 733052 | 192 | 192 | 192 | 153 | 114.00000 | 74.00000 | 309.0000 | 5822.000 | 248.0000 | 8.00000 | 240.0000 | 53.00000 | 93.00000 | 197.0000 | 38.00000 | 783.0000 | 82.00000 | 55.00000 | 12.00000 | 236.0000 | 170.00000 | 187.0000 | 220.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1025.6667 | 20.50000 | 29.833333 | 18.333333 | 24.83333 | 73.00000 | 48.66667 | 21.66667 | 16.54892 | 0.8366600 | 1.1690452 | 1.0327956 | 1.4719601 | 0.6324555 | 0.5163978 | 1.0327956 |
| 1376813 | 1021 | 912 | 1112 | 153 | 59.50000 | 82.00000 | 263.5000 | 8114.000 | 218.0000 | -93.50000 | 311.5000 | 164.50000 | -47.50000 | 167.0000 | -50.50000 | 922.5000 | 136.00000 | 44.50000 | 43.50000 | 405.0000 | 134.50000 | 349.5000 | 143.5000 | 6.3639610 | 0.0000000 | 0.7071068 | 38.183766 | 7.0710678 | 6.3639610 | 0.7071068 | 7.7781746 | 6.3639610 | 7.0710678 | 6.3639610 | 40.305087 | 5.6568542 | 2.1213203 | 0.7071068 | 15.556349 | 6.363961 | 12.020815 | 6.363961 | 693.8571 | 33.38095 | 23.285714 | 14.857143 | 92.42857 | 52.76190 | 46.66667 | 29.85714 | 23.58662 | 1.4654757 | 0.6436503 | 0.8535640 | 12.4119758 | 1.1359913 | 1.3165612 | 1.1526367 |
| 374525 | 9 | 9 | 9 | 153 | 86.00000 | 34.00000 | 184.0000 | 5337.000 | 184.0000 | 0.00000 | 184.0000 | 73.00000 | 54.00000 | 163.0000 | 16.00000 | 803.0000 | 97.00000 | 41.00000 | 28.00000 | 287.0000 | 134.00000 | 214.0000 | 156.0000 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 720.0000 | 20.16667 | 18.666667 | 10.166667 | 57.00000 | 59.33333 | 28.16667 | 53.16667 | 45.76899 | 2.1369761 | 1.6329932 | 1.3291601 | 12.3612297 | 1.6329932 | 1.4719601 | 3.0605010 |
The procedure used to obtain these environmental predictors is described here (Click to download the report)
Bioclimatic variables (bio01-bio19) derive from CHELSA
Codes:
Bio1 = Annual Mean Temperature
Bio2 = Mean Diurnal Range
Bio3 = Isothermality
Bio4 = Temperature Seasonality
Bio5 = Max Temperature of Warmest Month
Bio6 = Min Temperature of Coldest Month
Bio7 = Temperature Annual Range
Bio8 = Mean Temperature of Wettest Quarter
Bio9 = Mean Temperature of Driest Quarter
Bio10 = Mean Temperature of Warmest Quarter
Bio11 = Mean Temperature of Coldest Quarter
Bio12 = Annual Precipitation
Bio13 = Precipitation of Wettest Month
Bio14 = Precipitation of Driest Month
Bio15 = Precipitation Seasonality
Bio16 = Precipitation of Wettest Quarter
Bio17 = Precipitation of Driest Quarter
Bio18 = Precipitation of Warmest Quarter
Bio19 = Precipitation of Coldest Quarter
Soil variables (5 cm depth) derive from the ISRIC dataset, downloaded at 250-m resolution
CECSOL Cation Exchange capacity of soil
CLYPPT Clay mass fraction in %
CRFVOL Coarse fragments volumetric in %
ORCDRC Soil Organic Carbon Content in g/kg
PHIHOX Soil pH x 10 in H20
SLTPPT Silt mass fraction in %
SNDPPT Sand mass fraction in %
BLDFIE Bulk Density (fine earth) in kg/m3
P.ret.cat Phosphorous Retention - Categorical value, see ISRIC 2011-06
5 Export & SessionInfo
save( woody_species_traits, DT.xylem, CWM.xylem, header.xylem,
file="_derived/Xylem_sPlot.RData" )
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.7 LTS
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.18.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] rgeos_0.5-5 rgdal_1.5-17 sf_0.9-3 sp_1.4-4
## [5] downloader_0.4 gridExtra_2.3 viridis_0.5.1 viridisLite_0.3.0
## [9] kableExtra_1.3.1 knitr_1.30 forcats_0.5.0 stringr_1.4.0
## [13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2
## [17] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.5 lubridate_1.7.9 lattice_0.20-41 class_7.3-17
## [5] assertthat_0.2.1 digest_0.6.25 R6_2.5.0 cellranger_1.1.0
## [9] backports_1.1.10 reprex_0.3.0 evaluate_0.14 e1071_1.7-3
## [13] highr_0.8 httr_1.4.2 pillar_1.4.3 rlang_0.4.8
## [17] readxl_1.3.1 rstudioapi_0.11 blob_1.2.1 rmarkdown_2.5
## [21] labeling_0.3 webshot_0.5.2 munsell_0.5.0 broom_0.7.0
## [25] compiler_3.6.3 modelr_0.1.6 xfun_0.19 pkgconfig_2.0.3
## [29] htmltools_0.5.0 tidyselect_1.1.0 codetools_0.2-17 fansi_0.4.1
## [33] crayon_1.3.4 dbplyr_1.4.4 withr_2.3.0 jsonlite_1.7.1
## [37] gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0 magrittr_1.5
## [41] units_0.6-7 scales_1.1.1 KernSmooth_2.23-18 cli_2.1.0
## [45] stringi_1.5.3 farver_2.0.3 fs_1.5.0 xml2_1.3.2
## [49] ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.4 tools_3.6.3
## [53] glue_1.4.2 maps_3.3.0 hms_0.5.3 yaml_2.2.1
## [57] colorspace_1.4-1 classInt_0.4-3 rvest_0.3.6 haven_2.3.1