Timestamp: Fri Nov 13 17:37:07 2020
Drafted: Francesco Maria Sabatini
Version: 1.1

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(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

#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) |
          grepl(pattern = "tree|shrub", x = GrowthForm) |
          is.tree.or.tall.shrub==T
              ) %>% 
  #counter proof - exclude species == 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              58            7928 
##      shrub/tree     shrub\\tree            tree            <NA> 
##             105              29           13555              92

#
# MEMO for FMS: some standardization needed in sPlot 3.0 for GF names

Example of gap-filled trait data from TRY (20 randomly selected species)
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
Kunzea ericifolia	shrub	FALSE	1	0.7283649	NA	61.990281	NA	31.98463	NA	10.605079	NA	0.5061563	NA	195.2870	543.5571	NA	NA	3554.2440
Epacris paludosa	shrub	FALSE	1	0.6180130	NA	188.437623	NA	15.30076	NA	8.228136	NA	0.6297734	NA	368.1810	686.6200	NA	NA	1615.6063
Codia jaffrei	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Mortoniella pittieri	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Trophis	tree	TRUE	160	0.5799697	0.0778658	9.840891	1.1314241	50.44614	11.1199527	21.133250	6.0488715	15.1032393	3.8993275	327.6145	801.9136	30.6755155	50.3768197	1720.2909
Planchonella glauca	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Cotyledon velutina	shrub	FALSE	1	0.3137810	NA	620.284468	NA	21.22847	NA	16.053577	NA	1.1066924	NA	450.2601	512.9229	NA	NA	2530.9735
Mitrephora teysmannii	tree	TRUE	3	0.7483330	0.0090990	20.357402	0.5531617	94.16796	3.9737192	19.631506	0.5069761	12.5924995	0.5909462	493.0227	1171.1943	14.5568411	17.6612858	1268.6842
Neisosperma mianum	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Oxandra	tree	TRUE	508	0.7439664	0.0279287	46.542459	11.2841074	66.58668	3.3169535	11.912526	1.9821599	14.3297582	2.5364250	489.8337	1049.0371	25.3409824	46.4265455	1158.0969
Rhus typhina	tree	TRUE	61	0.4527526	0.0069034	53.477904	5.7865638	90.87015	6.9805694	16.845202	4.3542703	6.3294372	2.1204912	1441.3973	1983.9657	110.9144264	116.2715236	757.1311
Abies nephrolepis	tree	TRUE	27	0.3512129	0.0114178	407.180257	33.9504526	13.67868	0.4732980	9.892645	1.5514761	33.9981397	5.3546088	712.9564	1296.2292	35.6846481	38.7653154	2597.8812
Iryanthera	tree	TRUE	872	0.5788963	0.0214108	11.691323	2.6742994	69.60219	10.3527934	11.394669	1.7863227	18.2076192	2.6997193	615.3043	1615.3044	38.6066697	102.6116320	1203.0586
Licaria pucheri	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Ficus palmata	shrub	TRUE	3	0.4166359	0.0059615	17.196187	5.9575747	43.09359	1.5349340	18.205922	0.4136751	11.8499790	0.4458733	298.8537	572.8625	11.1446281	10.4592304	1377.5573
Santalum paniculatum	tree	TRUE	2	0.7417319	0.0000636	19.537087	0.9933779	24.31650	0.0772736	5.509882	0.0290659	4.9944134	0.0294042	240.6449	616.3328	0.0361808	0.9732207	554.3731
Diospyros cinnabarina	tree	TRUE	2	0.6610894	0.0035850	14.580362	0.9772326	55.77311	1.9367853	11.881875	0.1371734	10.9291313	0.7287004	431.2067	1151.1902	12.7213760	14.2037536	1452.2787
Protium altsonii	tree	TRUE	39	0.6128564	0.0255524	12.230485	0.9091390	43.51911	2.2129142	14.697606	2.1798936	20.4438840	1.7465118	404.9270	971.3543	13.3303058	26.8407913	765.7623
Litsea glutinosa	tree	TRUE	6	0.5487119	0.0540633	15.661711	0.3416939	22.81439	0.7334361	13.030198	0.3315966	14.1280203	0.4921995	454.1707	811.2948	11.0344099	7.2770561	534.8413
Anabasis elatior	shrub	FALSE	1	0.5692733	NA	60.585659	NA	33.58964	NA	4.135293	NA	0.2021352	NA	488.6956	1010.9334	NA	NA	2705.0498

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] 8547337

Merge 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 1 for each plot x layer combination
  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) %>%
  # merge layers together
  summarize(Relative.cover=combine.cover(Relative.cover), .groups="drop") %>%
  ungroup() %>% 
  # normalize relative cover to 1 after merging layers together
  left_join({.} %>% 
              group_by(PlotObservationID) %>% 
              summarize(Tot.Cover=sum(Relative.cover), .groups="drop"), 
            by="PlotObservationID") %>% 
  mutate(Relative.cover=Relative.cover/Tot.Cover) 

nrow(DT.xylem)

## [1] 7304230

double check that covers are properly standardized

DT.xylem %>% 
  filter(PlotObservationID %in% sample(header.xylem$PlotObservationID, 10, replace=F)) %>% 
  group_by(PlotObservationID) %>% 
  summarize(tot.cover=sum(Relative.cover), .groups="drop")

## # A tibble: 5 x 2
##   PlotObservationID tot.cover
##               <dbl>     <dbl>
## 1            143374         1
## 2            451004         1
## 3            685954         1
## 4            746410         1
## 5           1133820         1

3 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)

Example of CWM data file
PlotObservationID	trait	trait.value	trait.coverage	trait.variance	trait.nspecies	sp.richness	trait.coverage.nspecies
5	Ks	1.6950265	0.1428571	NA	1	4	0.2500000
5	P50	-1.5003056	0.1428571	NA	1	4	0.2500000
16	Ks	0.1557417	0.4838710	0.0014506	2	7	0.2857143
16	P50	-2.3015323	0.5000000	0.5938076	3	7	0.4285714
17	Ks	0.3915309	0.6385457	0.1519621	5	7	0.7142857
17	P50	-3.2692863	0.6385457	3.5600373	5	7	0.7142857
18	Ks	0.4580143	0.1861111	0.0410046	4	10	0.4000000
18	P50	-3.8911671	0.8333333	5.0350743	8	10	0.8000000
19	Ks	0.7376319	0.3353176	0.1460626	5	14	0.3571429
19	P50	-3.3159887	0.6514433	1.2686962	9	14	0.6428571
20	Ks	0.3050261	0.0175439	0.0160221	2	7	0.2857143
20	P50	-2.7121667	0.1842105	1.0054947	3	7	0.4285714
21	Ks	0.3050261	0.5000000	0.0160221	2	8	0.2500000
21	P50	-3.8529856	0.8877551	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	0.4107143	0.0210918	2	8	0.2500000
26	P50	-3.8980845	0.5576923	1.2427961	5	8	0.6250000
27	Ks	0.1500000	0.0157895	NA	1	7	0.1428571
27	P50	-4.0094507	0.0263158	5.2985022	2	7	0.2857143

CWM.xylem08 <- CWM.xylem %>%
  filter(trait.coverage>=0.8)

Number of plots with >=.8 coverage per trait
trait	num.plots
Ks	92391
P50	328855

#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 of header data

Header file - Columns present and % completeness
	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 1932708 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)

Data Origin
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	Ricarda Pätsch
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	Stephan M. Hennekens
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) %>% 
  dplyr::rename(Elevation=Elevation_median) %>% 
  dplyr::select(-Elevation_q2.5, -Elevation_q97.5, -Elevation_DEM.res)

Example of climatic and soil variables for 20 randomly selected plots. All values represent the mean in a circle centered on the plot coordinates, having a radius equal to the plot’s location uncertainty (capped to 50 km for computing reasons). Standard deviation (sd) is also reported for each climatic and soil variable.
PlotObservationID	Elevation	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
1055814	0	104.00000	60.00000	288.0000	5231.667	217.0000	8.166667	208.3333	54.00000	82.000000	178.0000	34.000000	814.1667	79.00000	47.00000	15.83333	234.3333	154.00000	231.0000	174.0000	0.000000	0.0000000	0.0000000	3.265986	0.0000000	0.4082483	0.5163978	0.0000000	0.000000	0.0000000	0.0000000	0.9831921	0.0000000	0.0000000	0.4082483	0.8164966	0.000000	0.000000	0.000000	500.9677	26.01613	29.258064	6.806452	64.22581	65.66129	37.290323	33.54839	44.646505	4.0265184	3.6839727	1.4467455	26.123552	1.6980563	2.5310342	3.4720307
14866	509	83.96739	77.72826	276.5435	7176.413	230.1304	-50.184783	280.4348	176.94565	-9.423913	180.4239	-13.391304	640.8913	87.08696	32.33696	36.25000	259.3696	99.84783	233.3370	108.9022	8.772398	0.4472937	1.1617819	46.601927	9.2916332	8.0122562	1.2692851	9.3083492	8.164832	9.2275670	8.1158596	87.2503606	12.0745415	4.5241165	1.8905491	36.3274840	14.101621	32.983938	15.855155	799.4233	20.34871	17.803935	18.152646	49.68385	53.77001	41.138399	41.04071	77.252901	2.9640025	2.3305197	1.8310616	15.822520	3.9467806	2.0002998	3.2557389
859556	NA	92.00000	66.00000	278.0000	6061.000	222.0000	-15.000000	236.0000	171.00000	35.000000	177.0000	12.000000	758.0000	76.00000	50.00000	13.00000	221.0000	164.00000	213.0000	168.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	673.6000	19.40000	21.600000	12.200000	43.80000	57.80000	55.000000	23.60000	25.822471	1.6733201	0.5477226	0.4472136	11.541230	2.2803509	1.8708287	2.1908902
1605521	107	98.00000	64.00000	328.0000	4570.000	209.0000	13.000000	196.0000	54.00000	41.000000	165.0000	41.000000	634.0000	59.00000	41.00000	9.00000	175.0000	135.00000	169.0000	135.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	917.1667	20.66667	26.333333	13.000000	36.00000	63.50000	39.500000	34.33333	20.778996	1.2110601	1.3662601	0.6324555	2.280351	0.8366600	1.0488088	0.8164966
1316079	NA	59.00000	73.00000	265.0000	7245.000	204.0000	-73.000000	277.0000	153.00000	-36.000000	156.0000	-39.000000	1045.0000	160.00000	48.00000	48.00000	480.0000	149.00000	434.0000	160.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	762.0000	25.00000	18.000000	14.000000	102.00000	52.00000	46.000000	37.00000	NA	NA	NA	NA	NA	NA	NA	NA
951628	183	97.27670	38.69660	303.1675	3181.583	167.3617	39.783981	127.5655	68.44903	97.684466	144.5801	58.339806	1440.9320	158.89320	81.16990	22.96359	465.4199	246.85922	335.3762	376.3786	4.773318	1.7585905	6.6904886	36.133848	4.6085486	5.1709873	2.8758710	5.6097607	4.820622	4.5956599	4.9357002	171.3306737	18.7292984	8.3564313	1.1698294	56.6295074	25.623696	41.839826	46.861626	468.7101	23.93448	17.202994	16.852865	149.33999	54.55216	36.391983	46.41243	83.179353	2.7960323	2.4750250	2.5543684	38.508144	2.7762622	2.5714244	3.6287941
1665327	1025	102.75000	69.46875	282.3125	6071.531	237.4375	-8.562500	246.0625	47.90625	191.625000	192.4688	25.343750	854.7188	110.37500	51.03125	25.87500	319.8750	153.68750	153.6875	213.0625	9.801251	0.5070073	1.6932027	29.491370	10.1566362	9.6283771	0.7156094	9.4232361	10.742589	10.3112353	9.4684855	67.8243625	8.7611680	4.4029636	1.4756081	26.1086440	13.294281	13.294281	17.159475	860.7004	28.14372	24.400810	18.368421	49.91498	62.89676	39.336032	36.29757	61.672393	2.3604223	2.6132133	2.2478059	17.741234	2.4683534	1.5693687	3.2293209
1985908	NA	184.00000	71.00000	378.0000	3881.000	292.0000	103.000000	189.0000	136.00000	233.000000	240.0000	135.000000	953.0000	197.00000	9.00000	86.00000	590.0000	30.00000	35.0000	499.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1017.0000	12.00000	16.000000	20.000000	20.00000	60.00000	9.000000	75.00000	NA	NA	NA	NA	NA	NA	NA	NA
1803757	687	83.80000	59.60000	186.2000	9002.800	235.4000	-84.800000	320.4000	207.80000	-43.800000	207.8000	-43.800000	1329.2000	254.20000	38.40000	64.80000	736.6000	134.00000	736.6000	134.0000	4.324350	0.5477226	0.4472136	24.488773	4.5607017	3.9623226	0.8944272	4.3243497	3.962323	4.3243497	3.9623226	87.7251389	16.2388423	3.1304952	1.6431677	48.9826500	10.246951	48.982650	10.246951	890.0000	27.71233	16.767123	14.726027	107.89041	54.00000	37.383562	46.06849	34.460525	1.6706573	1.0995018	1.6688342	12.763404	0.9279607	1.4398123	1.1344206
490220	33	85.00000	26.00000	136.0000	5928.000	187.0000	-7.000000	194.0000	170.00000	18.000000	170.0000	7.000000	588.0000	60.00000	31.00000	20.00000	178.0000	107.00000	178.0000	108.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	629.7143	20.71429	10.142857	6.000000	107.00000	50.00000	21.571429	68.14286	44.865407	0.4879500	0.8997354	0.5773503	7.461010	2.0000000	0.9759001	1.4638501
1796030	382	115.00000	49.00000	173.0000	8093.000	266.0000	-17.000000	283.0000	50.00000	123.000000	234.0000	7.000000	1920.0000	239.00000	90.00000	29.00000	690.0000	273.00000	528.0000	479.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	893.0000	30.75000	25.000000	16.750000	137.50000	52.25000	36.750000	38.75000	12.884099	0.5000000	0.0000000	0.9574271	5.259911	0.9574271	0.5000000	0.5000000
998764	50	190.00000	29.00000	162.0000	5140.000	286.0000	109.000000	177.0000	187.00000	253.000000	267.0000	125.000000	415.0000	83.00000	1.00000	76.00000	201.0000	5.00000	15.0000	133.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1321.1667	23.58333	32.833333	10.666667	18.75000	77.83333	39.166667	28.00000	41.908631	1.0836247	1.1146409	0.7784989	1.422226	0.7177406	0.8348471	0.9534626
1203804	27	99.00000	62.00000	287.0000	5399.000	215.0000	1.000000	214.0000	175.00000	77.000000	175.0000	27.000000	802.0000	79.00000	48.00000	15.00000	231.0000	155.00000	231.0000	169.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	432.8333	19.00000	2.166667	5.500000	79.83333	44.16667	9.666667	88.16667	59.334363	1.4142136	0.4082483	0.5477226	10.048217	0.7527727	0.8164966	0.9831921
775535	NA	97.00000	36.65556	198.7667	5202.522	195.7222	10.944444	184.8667	156.55556	69.000000	172.1111	28.055556	771.8111	80.72222	41.41111	22.55556	240.1556	133.45556	233.9222	147.4889	0.000000	0.7667399	3.1159250	14.984807	0.5614543	0.2303447	0.6902206	0.4996878	0.000000	0.3160303	0.2303447	7.6465377	0.4747034	0.6852285	0.4996878	1.4371563	1.703672	1.559514	1.921168	781.9368	21.41214	19.503161	11.867257	76.05815	66.99621	32.236410	48.23388	65.897185	3.5719802	2.6851071	3.4668524	22.104222	1.5779287	3.1867589	4.5277949
1947920	NA	95.00000	113.00000	277.0000	9919.000	316.0000	-92.000000	409.0000	125.00000	-25.000000	235.0000	-40.000000	176.0000	22.00000	8.00000	31.00000	62.0000	26.00000	40.0000	26.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1403.2000	19.00000	15.400000	14.800000	11.00000	79.20000	35.600000	48.80000	4.658326	0.7071068	0.8944272	1.7888544	0.000000	0.4472136	0.5477226	0.4472136
1456091	592	69.00000	75.00000	263.0000	7444.000	219.0000	-67.000000	286.0000	166.00000	-28.000000	169.0000	-32.000000	808.0000	99.00000	47.00000	24.00000	281.0000	147.00000	253.0000	174.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	732.3333	21.50000	18.666667	16.000000	45.33333	53.83333	44.666667	36.83333	26.409594	1.7606817	0.8164966	0.6324555	7.840068	0.7527727	0.8164966	1.1690452
466498	84	80.00000	49.00000	236.0000	5644.000	194.0000	-15.000000	209.0000	64.00000	50.000000	161.0000	6.000000	819.0000	91.00000	45.00000	22.00000	271.0000	146.00000	217.0000	166.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	539.1429	11.42857	4.571429	7.428571	64.57143	52.85714	13.857143	81.00000	22.341505	0.7867958	0.5345225	0.5345225	3.552330	0.6900656	1.0690450	1.6329932
910522	34	89.96809	60.78723	257.8085	6233.691	219.3723	-16.382979	235.8298	172.15957	9.500000	177.7340	8.648936	618.7872	68.40426	34.88298	18.46809	201.6596	119.74468	191.5106	119.7872	0.176716	0.4114579	0.8585634	6.864317	0.5676596	0.4887197	0.5799202	0.4930308	4.160102	0.4442108	0.4798621	9.2908469	1.1669199	0.6020821	0.5016559	3.3873680	1.911685	3.130649	1.905692	584.1868	19.19886	9.005682	7.257812	81.09162	56.05043	18.286932	72.61861	91.020105	3.8092692	2.7163924	0.9720376	23.877101	3.5887984	2.6472391	4.3236340
1252128	NA	91.00000	66.00000	262.0000	6703.000	229.0000	-23.000000	252.0000	180.00000	4.000000	184.0000	4.000000	513.0000	61.00000	29.00000	24.00000	182.0000	93.00000	165.0000	93.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	664.0000	24.00000	15.000000	10.000000	95.00000	58.00000	26.000000	59.00000	NA	NA	NA	NA	NA	NA	NA	NA
779809	87	89.68977	64.75908	276.5330	6010.749	218.4373	-15.191419	233.5974	171.05776	66.966997	174.1155	10.978548	788.2426	78.61716	49.20792	15.05281	233.3251	158.39274	232.7343	168.6700	1.372502	0.4279976	0.5123918	14.772762	1.3805719	1.3964509	0.5659130	3.2643779	6.024926	1.4164991	1.3586096	61.0301557	6.8021851	3.5795106	0.8235282	19.6866762	10.953133	19.241837	12.760864	573.9951	15.82623	5.616689	7.527284	57.46521	48.99538	22.357928	71.89888	58.083351	2.5895106	2.2557509	1.4731410	17.054369	5.6925041	6.8538879	8.5774418

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, soilclim.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_1.5-18      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] utf8_1.1.4         assertthat_0.2.1   digest_0.6.25      R6_2.5.0          
##  [9] cellranger_1.1.0   backports_1.2.0    reprex_0.3.0       evaluate_0.14     
## [13] e1071_1.7-4        highr_0.8          httr_1.4.2         pillar_1.4.3      
## [17] rlang_0.4.8        readxl_1.3.1       rstudioapi_0.11    rmarkdown_2.5     
## [21] labeling_0.4.2     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   fansi_0.4.1        crayon_1.3.4      
## [33] dbplyr_2.0.0       withr_2.3.0        jsonlite_1.7.1     gtable_0.3.0      
## [37] lifecycle_0.2.0    DBI_1.1.0          magrittr_1.5       units_0.6-7       
## [41] scales_1.1.1       KernSmooth_2.23-18 cli_2.1.0          stringi_1.5.3     
## [45] farver_2.0.3       fs_1.5.0           xml2_1.3.2         ellipsis_0.3.1    
## [49] generics_0.1.0     vctrs_0.3.4        tools_3.6.3        glue_1.4.2        
## [53] maps_3.3.0         hms_0.5.3          yaml_2.2.1         colorspace_1.4-1  
## [57] classInt_0.4-3     rvest_0.3.6        haven_2.3.1

Project #31 - Data Extraction

1 Extract plots from sPlot based on species with xylem traits

2 Extract woody species

3 Calculate CWMs and trait coverage

4 Extract climate and soils data

5 Export & SessionInfo