Timestamp: Tue Mar 9 14:22:05 2021
Drafted: Francesco Maria Sabatini
Version: 1.2

Changes to version 1.2 - Corrected final selection of plots in header. Only plots with CWM info are retained, but no minimum trait coverage threshold is used.

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 5510288 records, across 1243899 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("LDMC"),
                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      herb/shrub herb/shrub/tree           other           shrub 
##               0              48               2              55            7826 
##      shrub/tree            tree            <NA> 
##             133           13458              66

#
# 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	LDMC_mean	LDMC_sd	SLA_mean	SLA_sd	PlantHeight_mean	PlantHeight_sd	Wood.vessel.length_mean	WoodFiberLength_mean	Wood.vessel.length_sd	WoodFiberLength_sd	SpecificRootLength_mean
Ladenbergia macrophylla	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Heisteria silvianii	tree	TRUE	8	0.6762992	0.0039185	39.971238	2.3217612	68.17812	1.2590447	0.4187767	0.0051907	9.446712	0.3804836	15.4541716	1.3085587	656.4609	1413.8549	16.905524	25.463370	1589.533
Holmbergia tweedii	shrub	FALSE	1	0.5592053	NA	66.367393	NA	37.51859	NA	0.2650414	NA	9.331602	NA	0.5372286	NA	368.9059	722.9950	NA	NA	3877.866
Lorentzianthus viscidus	shrub	FALSE	2	0.5012427	0.0016781	54.705967	5.7547491	51.20226	4.5698400	0.2511843	0.0030958	18.703215	0.5603608	0.4570992	0.0125571	510.4968	1054.5602	21.772039	37.362471	6070.531
Decodon verticillatus	shrub	FALSE	3	0.5897748	0.0020175	24.005301	0.2790654	63.22109	1.8803617	0.3311110	0.0010592	15.333479	0.0855408	2.4233108	0.0311564	507.0536	879.8216	11.233850	12.001534	1804.982
Eugenia sericifolia	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Trema micrantha	tree	TRUE	176	0.3277559	0.0315968	9.891342	0.7172357	30.89997	1.2852766	0.3013928	0.0274817	20.094386	5.1464276	5.7700172	3.2884235	168.8283	542.5530	5.636455	16.713892	8047.617
Indigofera pratensis	shrub	FALSE	1	0.7056221	NA	79.112989	NA	17.72589	NA	0.3908649	NA	9.450473	NA	1.0496519	NA	336.3511	695.5926	NA	NA	1723.567
Erica sicula	shrub	FALSE	3	0.5586180	0.0033580	140.972169	1.9528244	33.34904	0.3602492	0.3838020	0.0024163	7.919960	0.1110790	0.3204382	0.0236398	368.8125	526.3998	5.199629	5.644479	1910.942
Gymnosporia arbutifolia	shrub	FALSE	1	0.5926094	NA	43.735160	NA	58.12411	NA	0.4041459	NA	8.179975	NA	3.3344554	NA	467.9014	812.9055	NA	NA	1586.298
Colubrina californica	shrub	FALSE	1	0.7089661	NA	19.146630	NA	45.05276	NA	0.2977724	NA	16.361801	NA	1.5205370	NA	298.9593	761.9694	NA	NA	1227.350
Psychotria microbotrys	tree	TRUE	3	0.4465341	0.0008933	28.665664	0.7193217	74.37041	2.8193075	0.2745901	0.0017542	16.699685	0.4876918	1.8241852	0.1100895	507.1929	970.1061	23.180652	29.991476	3917.999
Chrysothamnus parryi	shrub	FALSE	40	0.5899503	0.0263214	74.026752	9.0053405	27.00712	18.3447501	0.3096080	0.0294865	13.561447	4.7727490	0.9318287	0.3828284	345.1405	772.6801	64.236758	249.633946	2035.198
Mendoncia combretoides	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Eucalyptus leptophleba	tree	TRUE	6	0.8983216	0.0301123	17.779656	0.4158652	31.32770	0.5986533	0.5047220	0.0026239	4.713942	0.0625239	14.7463629	0.5992100	239.4705	685.6999	3.791384	7.708404	1705.412
Boscia senegalensis	tree	TRUE	21	0.6063877	0.0065775	13.458022	0.5729693	111.50938	3.2017994	0.4059514	0.0115763	6.492858	0.5829151	9.2247665	0.3489693	337.1864	1393.1038	10.101772	13.739736	4170.179
Siparuna lepidota	shrub	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
Metrosideros perforata	shrub	FALSE	13	0.6660570	0.0024276	26.250216	1.1350435	33.19168	0.7916471	0.4658608	0.0032118	5.252154	0.0777398	5.5900771	1.2875581	329.6599	843.0825	6.925344	19.542284	4201.249
Acrotriche rigida	shrub	FALSE	9	0.6468565	0.0111561	218.319190	15.7158099	26.67313	1.7522792	0.4624648	0.0370097	5.623042	2.8218414	0.2331505	0.0407264	379.7983	572.4147	17.231533	19.085635	678.583
Myrcia heringii	tree	TRUE	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

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] 8547026

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] 7303930

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: 8 x 2
##   PlotObservationID tot.cover
## *             <dbl>     <dbl>
## 1             15529      1.00
## 2             33932      1   
## 3            304886      1   
## 4            371624      1   
## 5            406823      1   
## 6            880028      1   
## 7           1343439      1   
## 8           1841611      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::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)

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

ggtc <- ggplot(data=CWM.xylem) +
  geom_density(aes(trait.coverage, col=trait)) + 
  xlab("Trait coverage (abundance)") +
  theme_bw()

ggnsp <- ggplot(data=CWM.xylem) +
  geom_density(aes(trait.coverage.nspecies, col=trait)) + 
  xlab("Trait coverage (Presence absence)") +
  theme_bw()

cowplot::plot_grid(ggtc, ggnsp, nrow=1)

Density of plots with CWM info, for increasing trait coverage.

#Create list of plots for which CWM data is available
plot.sel2 <- CWM.xylem %>%
      distinct(PlotObservationID) %>% 
      pull(PlotObservationID)

header.xylem <- header.xylem %>% 
  filter(PlotObservationID %in% plot.sel2)

Evaluate completeness of header data

Header file - Columns present and % completeness
	Completeness_perc
GIVD ID	100.0000000
TV2 relevé number	100.0000000
ORIG_NUM	0.0000000
GUID	100.0000000
Longitude	100.0000000
Latitude	100.0000000
Location uncertainty (m)	95.1074577
Country	99.8797459
CONTINENT	100.0000000
sBiome	100.0000000
sBiomeID	100.0000000
Ecoregion	100.0000000
EcoregionID	100.0000000
Locality	59.7740630
Relevé area (m²)	72.2840109
Cover abundance scale	100.0000000
Date of recording	85.0595113
Plants recorded	99.9993791
Herbs identified (y/n)	3.1847681
Mosses identified (y/n)	26.9234710
Lichens identified (y/n)	17.4962511
elevation_dem	75.5433945
Altitude (m)	83.6372630
Aspect (°)	36.8752762
Slope (°)	45.8192028
Forest	70.9690806
Shrubland	70.9690806
Grassland	70.9690806
Wetland	70.9690806
Sparse.vegetation	70.9690806
Naturalness	45.6927393
ESY	68.2003380
Cover total (%)	17.8143761
Cover tree layer (%)	21.9656272
Cover shrub layer (%)	23.8610268
Cover herb layer (%)	36.6534994
Cover moss layer (%)	19.2709826
Cover lichen layer (%)	0.3776311
Cover algae layer (%)	0.0616794
Cover litter layer (%)	3.6399124
Cover open water (%)	0.1637195
Cover bare rock (%)	2.0438027
Height (highest) trees (m)	9.7244400
Height lowest trees (m)	0.6358359
Height (highest) shrubs (m)	6.4524308
Height lowest shrubs (m)	0.7119003
Aver. height (high) herbs (cm)	7.8689872
Aver. height lowest herbs (cm)	1.5863817
Maximum height herbs (cm)	2.2091780
Maximum height cryptogams (mm)	0.1291542

The process results in 966287 plots selected, for a total of 1932574 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	66	Anne D. Bjorkman
00-RU-001	1100	Vasiliy Martynenko
00-RU-002	978	Milan Chytrý
00-RU-006	612	Sergey Yamalov
00-RU-XXX	137	Olga Demina
00-TR-001	6615	Ali Kavgacı
00-TR-002	962	Deniz Işık Gürsoy
AF-00-001	79	Marco Schmidt
AF-00-003	80	Norbert Jürgens
AF-00-003	80	Norbert Jürgens
AF-00-006	1069	Miguel Alvarez
AF-00-008	207	Hjalmar S. Kühl
AF-00-009	189	Rasmus Revermann
AF-00-010	16	Petr Sklenar
AF-00-011	93	Bruno Hérault
AF-CD-001	18	Kim Sarah Jacobsen
AF-CM-001	57	Jiri Dolezal
AF-EG-XXX	1557	Mohamed Abd El-Rouf Mousa El-Sheikh
AF-ET-001	36	Desalegn Wana
AF-MA-001	190	Manfred Finckh
AF-NA-001	9	Ben Strohbach
AF-ZA-003	1731	John A.M. Janssen
AF-ZW-001	20	Cyrus Samimi
AS-00-001	4536	Tomáš Černý
AS-00-003	329	Arkadiusz Nowak
AS-BD-001	150	Mohammed A.S. Arfin Khan
AS-CN-001	80	Hongyan Liu
AS-CN-002	9	Karsten Wesche
AS-CN-003	27	Helge Bruelheide
AS-CN-004	380	Zhiyao Tang
AS-CN-007	839	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	622	Jiri Dolezal
AS-IR-001	490	Jalil Noroozi
AS-IR-006	1193	Hamid Gholizadeh
AS-JP-002	45765	Yasuhiro Kubota
AS-KG-001	167	Peter Borchardt
AS-KZ-001	17	Viktoria Wagner
AS-MN-001	142	Henrik von Wehrden
AS-RU-001	66	Victor Chepinoga
AS-RU-002	3045	Andrey Korolyuk
AS-RU-004	140	Norbert Hölzel
AS-RU-005	516	Igor Lavrinenko
AS-SA-001	17	Mohamed Abd El-Rouf Mousa El-Sheikh
AS-TJ-001	1	Kim André Vanselow
AS-TR-002	1144	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	12642	John Thomas Hunter
AU-AU-XXX	4404	Paul David Macintyre
AU-NC-001	137	Jérôme Munzinger
AU-NZ-001	15616	Susan K. Wiser
AU-PG-001	63	Timothy J.S. Whitfeld
EU-00-002	2732	Jürgen Dengler
EU-00-004	3486	Xavier Font
EU-00-004a	1390	Borja Jiménez-Alfaro
EU-00-004b	2184	Xavier Font
EU-00-004c	2834	Maria Pilar Rodríguez-Rojo
EU-00-004d	1434	Borja Jiménez-Alfaro
EU-00-004e	3616	Federico Fernández-González
EU-00-004f	9303	Federico Fernández-González
EU-00-004g	894	Rosario G Gavilán
EU-00-011	9322	Idoia Biurrun
EU-00-013	3980	Kiril Vassilev
EU-00-016	451	Corrado Marcenò
EU-00-017	164	John A.M. Janssen
EU-00-018	8550	Jonathan Lenoir
EU-00-019	10159	Kiril Vassilev
EU-00-020	975	Flavia Landucci
EU-00-021	3714	Andraž Carni
EU-00-022	3567	Tomáš Peterka
EU-00-023	6251	Juan Antonio Campos
EU-00-024	4548	Idoia Biurrun
EU-00-026	6756	Gianmaria Bonari
EU-00-027	12982	Anni Pyykönen
EU-00-028	2640	Filip Küzmič
EU-AL-001	221	Michele De Sanctis
EU-AT-001	36399	Wolfgang Willner
EU-BE-002	15285	Els De Bie
EU-BG-001	3211	Iva Apostolova
EU-CH-005	14181	Thomas Wohlgemuth
EU-CH-011	4352	Ariel Bergamini
EU-CZ-001	48695	Milan Chytrý
EU-DE-001	15747	Florian Jansen
EU-DE-013	6726	Florian Jansen
EU-DE-014	25995	Ute Jandt
EU-DE-020	3099	Ricarda Pätsch
EU-DE-035	1640	Maike Isermann
EU-DE-040	805	Joachim Schrautzer
EU-DK-002	100321	Jesper Erenskjold Moeslund
EU-ES-001	473	Aaron Pérez-Haase
EU-FR-003	113339	Emmanuel Garbolino
EU-FR-005	5012	Jean-Claude Gegout
EU-GB-001	14944	John S. Rodwell
EU-GB-XXX	236	Irina Tatarenko
EU-GR-001	2290	Erwin Bergmeier
EU-GR-005	4639	Panayotis Dimopoulos
EU-GR-006	3182	Ioannis Tsiripidis
EU-HR-001	1728	Zvjezdana Stančić
EU-HR-002	6155	Željko Škvorc
EU-HU-003	4037	János Csiky
EU-IE-001	10912	Úna FitzPatrick
EU-IT-001	6222	Roberto Venanzoni
EU-IT-010	3545	Laura Casella
EU-IT-011	18433	Emiliano Agrillo
EU-IT-019	511	Angela Stanisci
EU-LT-001	5124	Valerijus Rašomavičius
EU-LV-001	2303	Solvita Rūsiņa
EU-MK-001	661	Renata Ćušterevska
EU-NL-001	58172	Stephan M. Hennekens
EU-PL-001	41565	Zygmunt Kącki
EU-PL-003	3849	Remigiusz Pielech
EU-RO-007	9586	Adrian Indreica
EU-RO-008	11700	Eszter Ruprecht
EU-RS-002	1399	Svetlana Aćić
EU-RS-003	3080	Mirjana Cuk
EU-RU-002	1410	Valentin Golub
EU-RU-003	138	Tatiana Lysenko
EU-RU-011	7327	Vadim Prokhorov
EU-RU-014	4713	Larisa Khanina
EU-SI-001	13955	Urban Šilc
EU-SK-001	24391	Milan Valachovič
EU-UA-001	1097	Anna Kuzemko
EU-UA-005	507	Tetiana Dziuba
EU-UA-006	1949	Viktor Onyshchenko
NA-00-002	1294	Luis Cayuela
NA-CA-003	38	Viktoria Wagner
NA-CA-004	147	Isabelle Aubin
NA-CA-005	89	Philippe Marchand
NA-CU-XXX	207	Ute Jandt
NA-GL-001	35	Birgit Jedrzejek
NA-US-002	54539	Robert K. Peet
NA-US-006	15292	Robert K. Peet
NA-US-008	551	Donald Waller
NA-US-014	1389	Donald A. Walker
NA-US-016	490	Dylan Craven
SA-00-002	170	Gwendolyn Peyre
SA-00-003	220	Glenda Mendieta-Leiva
SA-AR-002	230	Melisa Giorgis
SA-AR-003	139	Karina Speziale
SA-BO-003	73	Michael Kessler
SA-BR-002	1576	Alexander Christian Vibrans
SA-CL-002	213	Alvaro G. Gutiérrez
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

The data derive from 155 datasets.

4 Extract climate and soils data

soilclim.xylem <- soilclim %>% 
  filter(PlotObservationID %in% plot.sel2) %>% 
  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
1944710	3032	32.00000	127.00000	345.0000	7995.000	221.0000	-149.00000	370.0000	-31.00000	104.000000	142.0000	-77.000000	660.0000	78.00000	28.00000	26.00000	231.0000	96.0000	197.0000	186.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1122.8000	32.80000	21.800000	18.800000	68.40000	57.00000	40.60000	38.00000	30.227471	0.4472136	1.3038405	1.3038405	4.393177	0.0000000	1.1401754	1.4142136
1916052	817	88.00000	102.00000	311.0000	7696.000	280.0000	-48.00000	328.0000	-12.00000	205.000000	205.0000	-12.000000	685.0000	88.00000	21.00000	35.00000	259.0000	73.0000	73.0000	259.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	698.8333	27.83333	10.666667	12.833333	95.00000	56.33333	41.83333	47.66667	22.319647	1.9407902	0.5163978	3.4302575	6.228965	0.8164966	0.9831921	1.2110601
1494640	NA	98.02286	83.06286	279.8724	7372.890	251.9695	-45.42667	297.4533	183.65905	2.502857	197.7371	-3.135238	1300.6286	148.09524	66.92000	23.44952	412.3695	202.3600	391.9600	224.2705	8.659342	0.2429373	1.2634388	38.07615	9.177170	8.249963	1.061206	15.445915	8.202034	9.1269383	8.163103	162.74093	18.233810	9.374792	1.7593330	51.28681	28.424970	52.912937	32.00196	749.2215	25.95691	25.077279	12.944682	65.19468	59.35897	43.33737	31.57609	85.123443	3.4166064	3.3263059	2.6952591	16.276449	2.5497650	2.0668283	4.3713043
1819472	899	64.00000	51.00000	337.0000	3306.000	142.0000	-10.00000	152.0000	70.00000	17.000000	111.0000	17.000000	5336.0000	523.00000	358.00000	12.00000	1521.0000	1102.0000	1210.0000	1102.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	727.8000	39.40000	12.000000	31.200000	177.20000	52.20000	35.60000	52.60000	39.066610	2.4083189	0.0000000	4.3243497	8.318654	0.4472136	0.8944272	1.1401754
331350	48	84.00000	44.00000	215.0000	5666.000	194.0000	-9.00000	204.0000	68.00000	54.000000	165.0000	10.000000	842.0000	94.00000	46.00000	22.00000	276.0000	149.0000	226.0000	172.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	559.0000	11.42857	6.285714	8.285714	47.14286	53.85714	16.28571	77.42857	47.881799	0.5345225	0.4879500	0.4879500	7.755183	1.3451854	1.2535663	0.9759001
165379	NA	101.97390	43.56770	319.9772	3293.302	177.3687	41.23654	136.1223	63.00163	105.662317	150.7276	61.659054	1195.7618	130.73899	69.11093	22.13377	384.0326	220.0538	276.8532	306.2219	1.082830	1.6639392	5.3635298	39.02486	1.422668	1.984705	2.885003	2.735926	8.490970	0.9160016	1.491667	48.30326	5.990221	2.629166	0.6895947	18.80444	7.337034	8.668814	16.55708	426.2734	30.31971	20.383066	17.837410	106.36792	60.43024	37.80235	41.81726	48.747379	3.3263461	2.0298615	2.3976066	33.435885	2.3509428	2.3089200	3.7560478
1455323	277	89.00000	75.00000	256.0000	7712.000	240.0000	-52.00000	291.0000	188.00000	20.000000	190.0000	-17.000000	606.0000	83.00000	29.00000	37.00000	248.0000	87.0000	221.0000	99.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1072.5000	19.16667	20.666667	6.500000	25.00000	60.50000	47.83333	31.16667	39.312848	1.1690452	0.8164966	0.8366600	2.449490	1.3784049	2.5625508	2.7868740
1420284	NA	41.00000	83.00000	285.0000	7137.000	191.0000	-98.00000	290.0000	135.00000	-51.000000	138.0000	-57.000000	1524.0000	186.00000	64.00000	32.00000	552.0000	204.0000	535.0000	215.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	693.4000	33.00000	16.000000	15.800000	134.60000	45.60000	37.00000	47.00000	24.865639	0.7071068	0.0000000	0.8366600	11.371016	0.5477226	0.0000000	0.7071068
987690	420	163.00000	46.00000	228.0000	5418.000	274.0000	71.00000	203.0000	156.00000	232.000000	244.0000	95.000000	474.0000	87.00000	3.00000	67.00000	213.0000	11.0000	23.0000	155.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1209.7500	27.50000	27.250000	19.750000	30.75000	73.50000	40.00000	32.50000	9.464847	1.0000000	0.9574271	1.5000000	1.892969	0.5773503	0.8164966	0.5773503
19891	NA	44.75875	81.71087	290.7735	6949.753	188.5875	-92.21731	280.8840	135.60221	-42.456722	138.6685	-50.036832	1213.5138	157.58379	64.15654	27.54696	453.2118	212.8232	439.3425	262.2210	26.501319	0.4537785	3.0846418	120.56563	27.925956	24.592410	3.391897	27.969016	26.919750	27.7613157	24.639664	268.00790	37.803872	14.942923	4.1178561	107.32854	49.422178	98.974710	59.75895	736.9489	29.52463	16.586394	20.538798	113.13913	54.03106	40.07854	43.29845	74.868860	4.2626615	2.6847856	3.7852525	40.576396	3.4188101	3.1288340	5.0610356
1785533	730	115.00000	69.00000	232.0000	7927.000	263.0000	-35.00000	298.0000	208.00000	11.000000	227.0000	8.000000	1549.0000	241.00000	38.00000	55.00000	715.0000	130.0000	650.0000	158.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	944.4000	25.20000	21.400000	18.600000	70.00000	53.40000	38.00000	40.80000	26.292585	0.8366600	0.8944272	1.1401754	4.301163	0.5477226	1.2247449	1.0954451
1321029	NA	76.00000	74.00000	261.0000	7488.000	224.0000	-60.00000	284.0000	173.00000	-23.000000	175.0000	-25.000000	637.0000	93.00000	29.00000	44.00000	279.0000	89.0000	255.0000	99.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	801.0000	19.00000	17.000000	13.000000	30.00000	54.00000	44.00000	40.00000	NA	NA	NA	NA	NA	NA	NA	NA
1906192	897	96.40987	82.34764	273.1845	7636.567	246.3112	-55.49356	301.7918	196.81760	66.401288	198.4764	-7.886266	1134.9013	125.17811	75.38627	15.74893	358.4871	227.7318	332.7146	262.2768	5.910209	0.4767326	0.9391471	36.98493	6.258909	5.290381	1.054238	6.255749	5.636280	6.2096422	5.241885	150.79271	17.559742	10.460008	0.7414378	49.12622	31.348868	43.442436	36.97896	939.7073	27.79152	19.648721	18.353701	92.49812	46.65612	43.04509	37.29327	78.770294	3.5633616	1.5622396	2.8756354	23.567323	2.9127945	1.9564479	2.6987173
1828009	1170	54.00000	54.00000	350.0000	3256.000	136.0000	-18.00000	154.0000	42.00000	102.000000	102.0000	10.000000	2355.0000	239.00000	130.00000	15.00000	697.0000	430.0000	430.0000	632.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	663.8000	40.80000	11.400000	20.400000	211.60000	50.00000	34.40000	54.20000	46.029339	5.1672043	0.5477226	3.5777088	4.277850	1.4142136	1.1401754	1.3038405
1541800	331	144.32179	70.16293	290.3829	5838.676	275.4236	33.86151	241.5519	170.82485	145.761711	231.7128	70.771894	641.0998	74.98778	31.28921	25.28513	218.9817	108.4725	138.9532	128.2668	11.000107	2.5518962	5.8281541	40.40745	10.581644	11.456090	4.017672	10.987464	76.646460	11.0949988	10.873151	96.12326	11.292939	5.807619	1.6487976	32.51426	19.256718	21.315986	22.22758	1230.4833	20.66854	23.452567	19.464729	31.76674	72.37166	37.60005	38.95468	120.219216	2.1869963	1.7558819	2.4716325	11.855030	3.6060499	1.7895185	2.3069776
1317768	NA	91.00000	70.00000	264.0000	7084.000	235.0000	-30.00000	265.0000	184.00000	-2.000000	187.0000	-2.000000	478.0000	69.00000	24.00000	33.00000	194.0000	76.0000	175.0000	76.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1043.0000	20.00000	15.000000	8.000000	38.00000	64.00000	22.00000	62.00000	NA	NA	NA	NA	NA	NA	NA	NA
385820	56	83.00000	48.00000	234.0000	5556.000	194.0000	-10.00000	205.0000	68.00000	53.000000	163.0000	10.000000	857.0000	99.00000	47.00000	25.00000	293.0000	151.0000	226.0000	171.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	611.3333	11.83333	6.166667	7.166667	38.66667	53.83333	15.33333	78.50000	36.626038	0.7527727	1.4719601	0.9831921	3.265986	0.7527727	1.7511901	3.0166206
173431	108	94.00000	58.00000	325.0000	4160.000	193.0000	16.00000	177.0000	55.00000	150.000000	155.0000	42.000000	734.0000	79.00000	49.00000	15.00000	233.0000	153.0000	169.0000	165.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	574.0000	22.00000	21.333333	14.666667	66.83333	54.50000	39.16667	39.50000	24.859606	1.0954451	0.8164966	0.5163978	3.970726	1.0488088	0.7527727	1.6431677
702783	695	93.00000	75.00000	315.0000	5619.000	226.0000	-10.00000	237.0000	35.00000	168.000000	174.0000	21.000000	1326.0000	141.00000	84.00000	14.00000	405.0000	263.0000	272.0000	395.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	653.8000	22.20000	14.400000	16.000000	92.00000	49.00000	33.00000	53.00000	18.566098	0.8366600	0.5477226	1.0000000	11.575837	0.7071068	0.7071068	0.7071068
584588	1703	48.00000	81.00000	303.0000	6340.000	194.0000	-74.00000	268.0000	72.00000	-32.000000	139.0000	-34.000000	1537.0000	143.00000	103.00000	10.00000	424.0000	340.0000	353.0000	352.0000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	743.8000	31.80000	19.600000	19.600000	125.60000	58.20000	35.80000	44.40000	18.833481	0.8366600	1.3416408	1.1401754	6.693280	0.8366600	0.4472136	1.8165902

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_1.5-23      sf_0.9-3          sp_1.4-5         
##  [5] downloader_0.4    gridExtra_2.3     viridis_0.5.1     viridisLite_0.3.0
##  [9] kableExtra_1.3.4  knitr_1.31        forcats_0.5.1     stringr_1.4.0    
## [13] dplyr_1.0.4       purrr_0.3.4       readr_1.4.0       tidyr_1.1.3      
## [17] tibble_3.0.1      ggplot2_3.3.0     tidyverse_1.3.0  
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.2         sass_0.3.1         maps_3.3.0         jsonlite_1.7.2    
##  [5] modelr_0.1.6       bslib_0.2.4        assertthat_0.2.1   highr_0.8         
##  [9] cellranger_1.1.0   yaml_2.2.1         gdtools_0.2.3      pillar_1.4.3      
## [13] backports_1.2.1    lattice_0.20-41    glue_1.4.2         digest_0.6.25     
## [17] rvest_0.3.6        colorspace_2.0-0   cowplot_1.1.1      htmltools_0.5.1.1 
## [21] pkgconfig_2.0.3    broom_0.7.0        haven_2.3.1        scales_1.1.1      
## [25] webshot_0.5.2      svglite_1.2.3.2    generics_0.1.0     farver_2.1.0      
## [29] ellipsis_0.3.1     withr_2.4.1        cli_2.3.1          magrittr_2.0.1    
## [33] crayon_1.4.1       readxl_1.3.1       evaluate_0.14      fs_1.5.0          
## [37] fansi_0.4.2        xml2_1.3.2         class_7.3-18       tools_3.6.3       
## [41] hms_1.0.0          lifecycle_1.0.0    munsell_0.5.0      reprex_1.0.0      
## [45] compiler_3.6.3     jquerylib_0.1.3    e1071_1.7-4        systemfonts_1.0.1 
## [49] rlang_0.4.10       classInt_0.4-3     units_0.7-0        rstudioapi_0.13   
## [53] labeling_0.4.2     rmarkdown_2.7      gtable_0.3.0       DBI_1.1.1         
## [57] R6_2.5.0           lubridate_1.7.10   utf8_1.1.4         KernSmooth_2.23-18
## [61] stringi_1.5.3      Rcpp_1.0.5         vctrs_0.3.6        dbplyr_2.1.0      
## [65] tidyselect_1.1.0   xfun_0.21

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