Timestamp: Sat Nov 28 11:18:00 2020
Drafted: Francesco Maria Sabatini
Revised: Helge Bruelheide
Version: 1.1

This report documents the construction of the DT table for sPlot 3.0. It is based on dataset sPlot_3.0.2, received on 24/07/2019 from Stephan Hennekens.

Caution: Layer information is not available for all species in each plot. In case of missing information Layer is set to zero.

Changes in version 1.1
1) Added explanation of fields
2) Fixed taxon_group of Friesodielsia
3) Only export the fields Ab_scale and Abundance

knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(readr)
library(xlsx)
library(knitr)
library(kableExtra)

#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")

Search and replace unclosed quotation marks and escape them. Run in Linux terminal

# escape all double quotation marks. Run in Linux terminal
# sed 's/"/\\"/g' sPlot_3_0_2_species.csv > sPlot_3_0_2_species_test.csv

Import data Table

DT table is the species x plot matrix, in long format.

DT0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_species_test.csv", 
                            delim="\t", 
                         col_type = cols(
                                PlotObservationID = col_double(),
                                Taxonomy = col_character(),
                                `Taxon group` = col_character(),
                                `Taxon group ID` = col_double(),
                                `Turboveg2 concept` = col_character(),
                                `Matched concept` = col_character(),
                                Match = col_double(),
                                Layer = col_double(),
                                `Cover %` = col_double(),
                                `Cover code` = col_character(),
                                x_ = col_double()
                              )
                         ) 
nplots <- length(unique(DT0$PlotObservationID))
nspecies <- length(unique(DT0$`Matched concept`))

Match plots with those in header

load("../_output/header_sPlot3.0.RData")
DT0 <- DT0 %>% 
  filter(PlotObservationID %in% unique(header$PlotObservationID))

The DT table includes 43103293 species * plot records, across 1978589 plots. Before taxonomic resolution, there are 107676 species .

Example of initial DT table (3 randomly selected plots shown)
PlotObservationID Taxonomy Taxon group Taxon group ID Turboveg2 concept Matched concept Match Layer Cover % Cover code x_
447771 NO-Europe_lenoir Vascular plant 1 Achillea millefolium subsp. millefolium Achillea millefolium subsp. millefolium 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Achillea ptarmica Achillea ptarmica 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Agrostis capillaris Agrostis capillaris 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Carex ovalis Carex leporina 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Cerastium fontanum subsp. vulgare var. vulgare Cerastium fontanum subsp. holosteoides 2 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Chenopodium album subsp. album Chenopodium album subsp. album 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Deschampsia cespitosa Deschampsia cespitosa 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Galeopsis tetrahit Galeopsis tetrahit 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Holcus lanatus Holcus lanatus 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Lolium perenne Lolium perenne 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Poa pratensis subsp. pratensis Poa pratensis subsp. pratensis 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Poa trivialis Poa trivialis 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Ranunculus acris Ranunculus acris 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Ranunculus repens Ranunculus repens 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Taraxacum sect. Ruderalia Taraxacum sect. Taraxacum 3 0 1 x NA
447771 NO-Europe_lenoir Vascular plant 1 Trifolium repens Trifolium repens 3 0 1 x NA
608448 FR-France_sophy Vascular plant 1 Arrhenatherum elatius Arrhenatherum elatius 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Asplenium adiantum-nigrum subsp. onopteris Asplenium adiantum-nigrum subsp. onopteris 0 0 2
NA
608448 FR-France_sophy Vascular plant 1 Chenopodium bonus-henricus Blitum bonus-henricus 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Cynosurus echinatus Cynosurus echinatus 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Digitalis purpurea Digitalis purpurea 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Epilobium montanum Epilobium montanum 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Fagus sylvatica Fagus sylvatica 3 1 38 3 NA
608448 FR-France_sophy Vascular plant 1 Galium rotundifolium Galium rotundifolium 3 0 3 1 NA
608448 FR-France_sophy Vascular plant 1 Geranium robertianum Geranium robertianum 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Hypochaeris taraxacoides Hypochaeris taraxacoides 0 0 2
NA
608448 FR-France_sophy Vascular plant 1 Mycelis muralis Lactuca muralis 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Poa nemoralis Poa nemoralis 3 0 2
NA
608448 FR-France_sophy Vascular plant 1 Stellaria media Stellaria media 3 0 3 1 NA
1607503 BR-Britain Vascular plant 1 Agrostis stolonifera Agrostis stolonifera 3 6 8 8 NA
1607503 BR-Britain Vascular plant 1 Alopecurus geniculatus Alopecurus geniculatus 3 6 6 6 NA
1607503 BR-Britain Vascular plant 1 Alopecurus pratensis Alopecurus pratensis 3 6 10 10 NA
1607503 BR-Britain Vascular plant 1 Anthoxanthum odoratum Anthoxanthum odoratum 3 6 10 10 NA
1607503 BR-Britain Vascular plant 1 Cardamine pratensis Cardamine pratensis 3 6 2 2 NA
1607503 BR-Britain Vascular plant 1 Cerastium fontanum Cerastium fontanum 3 6 1 1 NA
1607503 BR-Britain Vascular plant 1 Glyceria fluitans Glyceria fluitans 3 6 15 15 NA
1607503 BR-Britain Vascular plant 1 Poa trivialis Poa trivialis 3 6 45 45 NA
1607503 BR-Britain Vascular plant 1 Polygonum amphibium Persicaria amphibia 3 6 1 1 NA
1607503 BR-Britain Vascular plant 1 Ranunculus acris Ranunculus acris 3 6 1 1 NA
1607503 BR-Britain Vascular plant 1 Ranunculus repens Ranunculus repens 3 6 4 4 NA
1607503 BR-Britain Vascular plant 1 Rumex acetosa Rumex acetosa 3 6 1 1 NA

Match species names from DT0 to those in Backbone

Import taxonomic backbone

load("../_output/Backbone3.0.RData")

Match to DT0, using Taxonomic concept as matching key. This is the field that was used to build, and resolve, the Backbone.

DT1 <- DT0 %>% 
  left_join(Backbone %>% 
              dplyr::select(Name_sPlot_TRY, Name_short, `Taxon group`, Rank_correct) %>%
              rename(`Matched concept`=Name_sPlot_TRY,
                     Taxongroup_BB=`Taxon group`), 
            by="Matched concept") %>% 
  # Simplify Rank_correct
  mutate(Rank_correct=fct_collapse(Rank_correct, 
                                   lower=c("subspecies", "variety", "infraspecies", "race", "forma"))) %>% 
  mutate(Rank_correct=fct_explicit_na(Rank_correct, "No_match")) %>% 
  mutate(Name_short=replace(Name_short, 
                            list=Name_short=="No suitable", 
                            values=NA))

Explore name matching based on Backbone v1.2

Select species entries that changed after taxonomic standardization, as a way to check the backbone.

name.check <- DT1 %>% 
  dplyr::select(`Turboveg2 concept`:`Matched concept`, Name_short) %>% 
  rename(Name_TNRS=Name_short) %>% 
  distinct() %>% 
  mutate(Matched_short=word(`Matched concept`, start = 1L, end=2L)) %>% 
  filter(is.na(Name_TNRS) | Matched_short != Name_TNRS) %>%
  dplyr::select(-Matched_short) %>% 
  arrange(Name_TNRS)
Check 30 random species names from DT that changed name after matching to backbone
Turboveg2 concept Matched concept Name_TNRS
Silber lanzettblatt breit 136307 Silber lanzettblatt breit 136307 NA
Anthosachne scabra Anthosachne scabra Elymus scabrus
Koeleria cristata subsp. gracilis Koeleria cristata subsp. gracilis Koeleria macrantha
Frustulia rhomboides var. saxonica Frustulia rhomboides var. saxonica Frustulia
Hypericum elongatum subsp. microcalycinum Hypericum elongatum subsp. microcalycinum Hypericum microcalycinum
Tortula intermedia Tortula intermedia Syntrichia montana
Randia formosa Randia formosa Rosenbergiodendron formosum
Papaver species Papaver species Papaver
Inga sp4_UMICH1 Inga sp4_UMICH1 Inga
Diospyros species [M11] Diospyros species [M11] Diospyros
Ficus radula Ficus radula Ficus obtusiuscula
CYPERACEAE SPECIES CYPERACEAE SPECIES Cyperaceae
Diospyros species [DIOFLE] Diospyros species [DIOFLE] Diospyros
Tribulopis pentandra Tribulopis pentandra Kallstroemia pentandra
Stipagrostis sp 134344A Stipagrostis sp 134344A Stipagrostis
Osmorhiza claytonii Osmorhiza claytonii Osmorhiza aristata
Tetrapora glomerata Tetrapora glomerata Baeckea pentandra
Torilis elongata Torilis elongata Torilis arvensis
Crossostylis species Crossostylis species Crossostylis
Asarum dimidiatum Asarum dimidiatum Asarum sieboldii
Ferdinandusa cf. elliptica Ferdinandusa cf. elliptica Ferdinandusa elliptica
Juncus gerardii subsp. persicus Juncus gerardii subsp. persicus Juncus persicus
Hypopitys_monotropa species Hypopitys_monotropa species Monotropa hypopitys
Acer morrisonense Acer morrisonense Acer caudatifolium
Pilosella hoppeana subsp. pilisquama Pilosella hoppeana subsp. testimonialis Pilosella pilisquama
Seedlings of Seedlings of NA
Heracleum alpinum x montanum Heracleum alpinum x montanum Heracleum sphondylium
Arabidopsis parvula Arabidopsis parvula Eutrema parvulum
Symphyotrichum species [lanceolatum + lateriflorum + racemosum] Symphyotrichum species [lanceolatum + lateriflorum + racemosum] Symphyotrichum lanceolatum
Sisyrinchium alatum Sisyrinchium alatum Sisyrinchium vaginatum

Check the most common species names from DT after matching to backbone

name.check.freq <- DT1 %>% 
  dplyr::select(`Turboveg2 concept`:`Matched concept`, Name_short) %>% 
  rename(Name_TNRS=Name_short) %>% 
  group_by(`Turboveg2 concept`, `Matched concept`, Name_TNRS) %>% 
  summarize(n=n()) %>% 
  mutate(Matched_short=word(`Matched concept`, start = 1L, end=2L)) %>% 
  filter(is.na(Name_TNRS) | Matched_short != Name_TNRS) %>%
  dplyr::select(-Matched_short) %>% 
  ungroup() %>% 
  arrange(desc(n)) 
## `summarise()` regrouping output by 'Turboveg2 concept', 'Matched concept' (override with `.groups` argument)
Check 40 most common species names from DT that changed name after matching to backbone
Turboveg2 concept Matched concept Name_TNRS n
Deschampsia flexuosa Avenella flexuosa Deschampsia flexuosa 126515
Festuca pratensis Schedonorus pratensis Festuca pratensis 84008
Elymus repens Elytrigia repens Elymus repens 82891
Phalaris arundinacea Phalaroides arundinacea Phalaris arundinacea 75296
Bryophyta species Bryophyta species NA 74393
Poa annua Ochlopoa annua Poa annua 67460
Potentilla anserina Argentina anserina Potentilla anserina 63786
Taraxacum sect. Ruderalia Taraxacum sect. Taraxacum Taraxacum 58429
Taraxacum species Taraxacum species Taraxacum 57167
Cornus sanguinea Cornus sanguinea Cornus controversa 52651
Elytrigia repens Elytrigia repens Elymus repens 51670
Taraxacum officinale Taraxacum sect. Taraxacum Taraxacum 50502
Weinmannia racemosa Weinmannia racemosa Leiospermum racemosum 38269
Bromus erectus Bromopsis erecta Bromus erectus 33765
Cladonia species Cladonia species Cladonia 32464
Avenella flexuosa Avenella flexuosa Deschampsia flexuosa 30787
Rubus sect. Rubus Rubus sect. Rubus Rubus 28684
Festuca arundinacea Schedonorus arundinaceus Festuca arundinacea 26124
Trientalis europaea Trientalis europaea Lysimachia europaea 25940
Rubus fruticosus aggr. Rubus fruticosus aggr. Rubus vestitus 23669
Glaux maritima Glaux maritima Lysimachia maritima 23306
Taraxacum officinale aggr. Taraxacum sect. Taraxacum Taraxacum 22837
Rubus species Rubus species Rubus 22098
Festuca gigantea Schedonorus giganteus Festuca gigantea 20917
Taraxacum sectie Ruderalia Taraxacum sect. Taraxacum Taraxacum 20888
Lophozonia menziesii Lophozonia menziesii Lophozonia 20249
Juncus gerardi Juncus gerardi Juncus gerardii 19094
Sphagnum species Sphagnum species Sphagnum 18293
Festuca rupicola Festuca stricta subsp. sulcata Festuca rupicola 18010
Rosa species Rosa species Rosa 16657
Podocarpus laetus Podocarpus laetus Podocarpus spinulosus 16356
Bromus tectorum Anisantha tectorum Bromus tectorum 16305
Carex species Carex species Carex 15744
Ripogonum scandens Ripogonum scandens Rhipogonum 14984
Rubus hirtus Rubus hirtus aggr. Rubus proiectus 14191
Avenula pubescens Avenula pubescens Helictotrichon pubescens 13490
Notogrammitis billardierei Notogrammitis billardierei NA 13117
Crataegus species Crataegus species Crataegus 13072
Helictotrichon pubescens Avenula pubescens Helictotrichon pubescens 12941
Erophila verna Draba verna Erophila verna 12646

Complete field taxon group

Taxon group information is only available for 35708898 entries, but absent for 7394395. To improve the completeness of this field, we derive additional info from the Backbone, and merge it with the data already present in DT.

table(DT1$`Taxon group`, exclude=NULL)
## 
##           Alga         Lichen           Moss       Mushroom      Stonewort 
##           9497         324080        2035007            513          12166 
##        Unknown Vascular plant 
##        7394395       33327635
DT1 <- DT1 %>% 
  mutate(`Taxon group`=ifelse(`Taxon group`=="Unknown", NA, `Taxon group`)) %>% 
  mutate(Taxongroup_BB=ifelse(Taxongroup_BB=="Unknown", NA, Taxongroup_BB)) %>% 
  mutate(`Taxon group`=coalesce(`Taxon group`, Taxongroup_BB)) %>% 
  dplyr::select(-Taxongroup_BB)


table(DT1$`Taxon group`, exclude=NULL)
## 
##           Alga         Lichen           Moss       Mushroom      Stonewort 
##           9991         366997        2090994            513          12166 
## Vascular plant           <NA> 
##       40532027          90605

Those taxa for which a measures of Basal Area exists can be safely assumed to belong to vascular plants

DT1 <- DT1 %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=`Cover code`=="x_BA", 
                               values="Vascular plant"))

Cross-complement Taxon group information. This means that, whenever a taxon is marked to belong to one group, then assign the same taxon to that group throughout the DT table.

DT1 <- DT1 %>% 
  left_join(DT1 %>% 
              filter(!is.na(Name_short)) %>% 
              filter(`Taxon group` != "Unknown") %>% 
              dplyr::select(Name_short, `Taxon group`) %>% 
              distinct(Name_short, .keep_all=T) %>% 
              rename(TaxonGroup_compl=`Taxon group`),
            by="Name_short") %>% 
  mutate(`Taxon group`=coalesce(`Taxon group`, TaxonGroup_compl)) %>% 
  dplyr::select(-TaxonGroup_compl)

table(DT1$`Taxon group`, exclude=NULL)
## 
##           Alga         Lichen           Moss       Mushroom      Stonewort 
##           9994         367586        2100627            513          12193 
## Vascular plant           <NA> 
##       40533605          78775

Check species with conflicting Taxon group information and fix manually.

#check for conflicts in attribution of genera to Taxon groups
DT1 %>% 
  filter(!is.na(Name_short)) %>% 
  filter(!is.na(`Taxon group`)) %>% 
  distinct(Name_short, `Taxon group`) %>% 
  mutate(Genus=word(Name_short,1)) %>% 
  dplyr::select(Genus, `Taxon group`) %>% 
  distinct() %>% 
  group_by(Genus) %>% 
  summarize(n=n()) %>% 
  filter(n>1) %>% 
  arrange(desc(n))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 15 x 2
##    Genus                 n
##    <chr>             <int>
##  1 Brachytheciastrum     2
##  2 Brachythecium         2
##  3 Chara                 2
##  4 Characeae             2
##  5 Hepatica              2
##  6 Hypericum             2
##  7 Hypnum                2
##  8 Leptorhaphis          2
##  9 Lychnothamnus         2
## 10 Nitella               2
## 11 Oxymitra              2
## 12 Pancovia              2
## 13 Peltaria              2
## 14 Tonina                2
## 15 Zygodon               2

Manually fix some known problems in Taxon group attribution. Some lists of taxa (e.g., lichen.genera, mushroom.genera) were defined when building the Backbone.

#Attach genus info
DT1 <- DT1 %>% 
    left_join(Backbone %>% 
              dplyr::select(Name_sPlot_TRY, Name_short) %>%
              mutate(Genus=word(Name_short, 1, 1)) %>%
              dplyr::select(-Name_short) %>% 
              rename(`Matched concept`=Name_sPlot_TRY),
            by="Matched concept") %>% 
    mutate(`Taxon group`=fct_collapse(`Taxon group`, 
                                    Alga_Stonewort=c("Alga", "Stonewort")))
#manually fix some known problems
mosses.gen    <- c("Hypnum", "Brachytheciastrum","Brachythecium","Hypnum",  
                  "Zygodon", "Oxymitra", "Bryophyta", "Musci", '\\\"Moos\\\"')
vascular.gen  <- c("Polystichum", "Hypericum", "Peltaria", "Pancovia", "Calythrix", "Ripogonum",
                  "Notogrammitis", "Fuscospora", "Lophozonia",  "Rostellularia", 
                  "Hesperostipa", "Microsorium", "Angiosperm","Dicotyledonae", "Spermatophy", 
                  "Oxymitra", "Friesodielsia")
alga.gen      <- c("Chara", "Characeae", "Tonina", "Nostoc", "Entermorpha", "Hydrocoleum" )
 
DT1 <- DT1 %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% mosses.gen, 
                               values="Moss")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% vascular.gen, 
                               values="Vascular plant")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% alga.gen, 
                               values="Alga_Stonewort")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% c(lichen.genera, "Lichenes"),
                               values="Lichen")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% mushroom, 
                               values="Mushroom"))
  
table(DT1$`Taxon group`, exclude=NULL)
## 
## Alga_Stonewort         Lichen           Moss       Mushroom Vascular plant 
##          23098         367587        2100704            513       40535446 
##           <NA> 
##          75945

Delete all records of fungi, and use lists of genera to fix additional problems. While in the previous round the matching was done on the resolved Genus name, here the match is based on unresolved Genus names.

DT1 <- DT1 %>% 
  dplyr::select(-Genus) %>% 
  left_join(DT1 %>% 
              distinct(`Matched concept`) %>% 
              mutate(Genus=word(`Matched concept`, 1)), 
            by="Matched concept") %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                                 list=Genus %in% mushroom, 
                                 values = "Mushroom")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% lichen.genera, 
                               values="Lichen")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% mosses.gen, 
                               values="Moss")) %>% 
  mutate(`Taxon group`=replace(`Taxon group`, 
                               list=Genus %in% vascular.gen, 
                               values="Vascular plant")) %>% 
  mutate(`Taxon group` = fct_explicit_na(`Taxon group`, "Unknown")) %>% 
  filter(`Taxon group`!="Mushroom") %>%
  mutate(`Taxon group`=factor(`Taxon group`))
  #dplyr::select(-Genus)

table(DT1$`Taxon group`, exclude=NULL)
## 
## Alga_Stonewort         Lichen           Moss Vascular plant        Unknown 
##          23098         367933        2103361       40572743          35721

After cross-checking all sources of information, the number of taxa not having Taxon group information decreased to 35721 entries

Standardize abundance values

Species abundance information varies across datasets and plots. While for the large majority of plots abundance values are returned as percentage cover, there is a subset where abundance is returned with different scales. These are marked in the column Cover code as follows:
x_BA - Basal Area
x_IC - Individual count
x_SC - Stem count
x_IV - Relative Importance
x_RF - Relative Frequency
x - Presence absence
Still, it’s not really intuitive that in case Cover code belongs to one of the classes above, then the actual abundance value is stored in the x_ column. This stems from the way this data is stored in TURBOVEG.
To make the cover data more user friendly, I simplify the way cover it is stored, so that there are only two columns:
Ab_scale - to report the type of scale used
Abundance - to coalesce the cover\abundance values previously in the columns Cover % and x_.

# Create Ab_scale field
DT1 <- DT1 %>% 
  mutate(Ab_scale = ifelse(`Cover code` %in% 
                             c("x_BA", "x_IC", "x_SC", "x_IV", "x_RF") & !is.na(x_), 
                           `Cover code`, 
                           "CoverPerc"))  

Fix some errors. There are some plots where all species have zeros in the field Cover %. Some of them are marked as p\a (Cover code=="x"), but other not. Consider all this plots as presence\absence and transform Cover % to 1.

allzeroes <- DT1 %>% 
  group_by(PlotObservationID) %>% 
  summarize(allzero=all(`Cover %`==0) ) %>% 
  filter(allzero==T) %>% 
  pull(PlotObservationID)
## `summarise()` ungrouping output (override with `.groups` argument)
DT1 <- DT1 %>% 
  mutate(`Cover %`=replace(`Cover %`, 
                           list=(PlotObservationID %in% allzeroes), 
                           values=1)) %>% 
  mutate(`Cover code`=replace(`Cover code`, 
                           list=(PlotObservationID %in% allzeroes), 
                           values="x"))

Consider all plot-layer combinations where Cover code=="x", and all the entries of the field Cover % == 1 as presence\absence data, and transform Ab_scale to “pa”. This is done to avoid confusion with plots where Cover code=="x" but “x” has to be intended as a class in the cover scale used. For p\a plots, replace the field Cover % with NA, and assign the value 1 to the field x_.

#plots with at least one entry in Cover code=="x"
sel <- DT1 %>% 
  filter(`Cover code`=="x") %>% 
  distinct(PlotObservationID) %>% 
  pull(PlotObservationID)

DT1 <- DT1 %>% 
  left_join(DT1 %>%
              filter(PlotObservationID %in% sel) %>% 
              group_by(PlotObservationID, Layer) %>% 
              mutate(to.pa= all(`Cover %`==1 & `Cover code`=="x")) %>% 
              distinct(PlotObservationID, Layer, to.pa), 
            by=c("PlotObservationID", "Layer")) %>% 
  replace_na(list(to.pa=F)) %>% 
  mutate(Ab_scale=ifelse(to.pa==T, "pa", Ab_scale)) %>% 
  mutate(`Cover %`=ifelse(to.pa==T, NA, `Cover %`)) %>% 
  mutate(x_=ifelse(to.pa==T, 1, x_)) %>% 
  dplyr::select(-to.pa)

There are also some plots having different cover scales in the same layer. They are not many, and I will reduce their cover value to p\a.
Find these plots first:

mixed <- DT1 %>% 
  distinct(PlotObservationID, Ab_scale, Layer) %>% 
  group_by(PlotObservationID, Layer) %>% 
  summarize(n=n()) %>% 
  filter(n>1) %>% 
  pull(PlotObservationID) %>% 
  unique()
## `summarise()` regrouping output by 'PlotObservationID' (override with `.groups` argument)
length(mixed)
## [1] 335

Transform these plots to p\a and correct field Ab_scale. Note: the column Abundance is only created here.

DT1 <- DT1 %>% 
  mutate(Ab_scale=replace(Ab_scale, 
                           list=PlotObservationID %in% mixed, 
                           values="mixed")) %>%
  mutate(`Cover %`=replace(`Cover %`, 
                           list=Ab_scale=="mixed",
                           values=NA)) %>% 
  mutate(x_=replace(x_,  list=Ab_scale=="mixed", values=1)) %>% 
  mutate(Ab_scale=replace(Ab_scale, list=Ab_scale=="mixed", values="pa")) %>% 
  #Create additional field Abundance to avoid overwriting original data
  mutate(Abundance =ifelse(Ab_scale %in% c("x_BA", "x_IC", "x_SC", "x_IV", "x_RF", "pa"), 
                          x_, `Cover %`)) %>% 
  mutate(Abundance=replace(Abundance, 
                           list=PlotObservationID %in% mixed, 
                           values=1))

Double check and summarize Ab_scales

scale_check <- DT1 %>% 
  distinct(PlotObservationID, Layer, Ab_scale) %>% 
  group_by(PlotObservationID) %>% 
  summarise(Ab_scale_combined=ifelse(length(unique(Ab_scale))==1, 
                                     unique(Ab_scale), 
                                     "Multiple_scales"))
## `summarise()` ungrouping output (override with `.groups` argument)
nrow(scale_check)== length(unique(DT1$PlotObservationID))
## [1] TRUE
table(scale_check$Ab_scale_combined)
## 
##       CoverPerc Multiple_scales              pa            x_BA            x_IC 
##         1691447            2084          271057            6293            2092 
##            x_IV            x_RF            x_SC 
##             146             585            4878

Calculate species’ relative covers in each plot

Transform abundances to relative abundance. For consistency with the previous version of sPlot, this field is called Relative_cover.
Watch out - Even plots with p\a information are transformed to relative cover.

DT1 <- DT1 %>% 
  left_join(x=., 
            y={.} %>%
              group_by(PlotObservationID) %>% 
              summarize(tot.abundance=sum(Abundance)), 
            by=c("PlotObservationID")) %>% 
  mutate(Relative.cover=Abundance/tot.abundance)
## `summarise()` ungrouping output (override with `.groups` argument)
# check: there should be no plot where the sum of all relative covers !=0
DT1 %>% 
  group_by(PlotObservationID) %>% 
  summarize(tot.cover=sum(Relative.cover), 
            num.layers=sum(unique(Layer))) %>% 
  filter(tot.cover != num.layers) %>% 
  nrow()
## `summarise()` ungrouping output (override with `.groups` argument)
## [1] 1958809

Clean DT and export

DT2 <- DT1 %>% 
  dplyr::select(PlotObservationID, Name_short, `Turboveg2 concept`, Rank_correct, `Taxon group`, Layer:x_, Ab_scale, Abundance, Relative.cover ) %>% 
  rename(Species_original=`Turboveg2 concept`, 
         Species=Name_short,
         Taxon_group=`Taxon group`, 
         Cover_perc=`Cover %`, 
         Cover_code=`Cover code`, 
         Relative_cover=Relative.cover) %>% 
  ## change in Version 1.1.
  dplyr::select(-x_, -Cover_perc)

The output of the DT table contains 43102856 records, over 1978582 plots. The total number of taxa is 116256 and 0, before and after standardization, respectively. Information on the Taxon group is available for 76548 standardized species.

Example of initial DT table (same 3 randomly selected plots shown above)
PlotObservationID Species Species_original Rank_correct Taxon_group Layer Cover_code Ab_scale Abundance Relative_cover
447771 Achillea millefolium Achillea millefolium subsp. millefolium lower Vascular plant 0 x pa 1 0.0625000
447771 Achillea ptarmica Achillea ptarmica species Vascular plant 0 x pa 1 0.0625000
447771 Agrostis capillaris Agrostis capillaris species Vascular plant 0 x pa 1 0.0625000
447771 Carex leporina Carex ovalis species Vascular plant 0 x pa 1 0.0625000
447771 Cerastium fontanum Cerastium fontanum subsp. vulgare var. vulgare lower Vascular plant 0 x pa 1 0.0625000
447771 Chenopodium album Chenopodium album subsp. album higher Vascular plant 0 x pa 1 0.0625000
447771 Deschampsia cespitosa Deschampsia cespitosa species Vascular plant 0 x pa 1 0.0625000
447771 Galeopsis tetrahit Galeopsis tetrahit species Vascular plant 0 x pa 1 0.0625000
447771 Holcus lanatus Holcus lanatus species Vascular plant 0 x pa 1 0.0625000
447771 Lolium perenne Lolium perenne species Vascular plant 0 x pa 1 0.0625000
447771 Poa pratensis Poa pratensis subsp. pratensis lower Vascular plant 0 x pa 1 0.0625000
447771 Poa trivialis Poa trivialis species Vascular plant 0 x pa 1 0.0625000
447771 Ranunculus acris Ranunculus acris species Vascular plant 0 x pa 1 0.0625000
447771 Ranunculus repens Ranunculus repens species Vascular plant 0 x pa 1 0.0625000
447771 Taraxacum Taraxacum sect. Ruderalia genus Vascular plant 0 x pa 1 0.0625000
447771 Trifolium repens Trifolium repens species Vascular plant 0 x pa 1 0.0625000
608448 Arrhenatherum elatius Arrhenatherum elatius species Vascular plant 0
CoverPerc 2 0.0312500
608448 Asplenium adiantum-nigrum Asplenium adiantum-nigrum subsp. onopteris species Vascular plant 0
CoverPerc 2 0.0312500
608448 Chenopodium bonus-henricus Chenopodium bonus-henricus species Vascular plant 0
CoverPerc 2 0.0312500
608448 Cynosurus echinatus Cynosurus echinatus species Vascular plant 0
CoverPerc 2 0.0312500
608448 Digitalis purpurea Digitalis purpurea species Vascular plant 0
CoverPerc 2 0.0312500
608448 Epilobium montanum Epilobium montanum species Vascular plant 0
CoverPerc 2 0.0312500
608448 Fagus sylvatica Fagus sylvatica species Vascular plant 1 3 CoverPerc 38 0.5937500
608448 Galium rotundifolium Galium rotundifolium species Vascular plant 0 1 CoverPerc 3 0.0468750
608448 Geranium robertianum Geranium robertianum species Vascular plant 0
CoverPerc 2 0.0312500
608448 Hypochaeris taraxacoides Hypochaeris taraxacoides species Vascular plant 0
CoverPerc 2 0.0312500
608448 Lactuca muralis Mycelis muralis species Vascular plant 0
CoverPerc 2 0.0312500
608448 Poa nemoralis Poa nemoralis species Vascular plant 0
CoverPerc 2 0.0312500
608448 Stellaria media Stellaria media species Vascular plant 0 1 CoverPerc 3 0.0468750
1607503 Agrostis stolonifera Agrostis stolonifera species Vascular plant 6 8 CoverPerc 8 0.0769231
1607503 Alopecurus geniculatus Alopecurus geniculatus species Vascular plant 6 6 CoverPerc 6 0.0576923
1607503 Alopecurus pratensis Alopecurus pratensis species Vascular plant 6 10 CoverPerc 10 0.0961538
1607503 Anthoxanthum odoratum Anthoxanthum odoratum species Vascular plant 6 10 CoverPerc 10 0.0961538
1607503 Cardamine pratensis Cardamine pratensis species Vascular plant 6 2 CoverPerc 2 0.0192308
1607503 Cerastium fontanum Cerastium fontanum species Vascular plant 6 1 CoverPerc 1 0.0096154
1607503 Glyceria fluitans Glyceria fluitans species Vascular plant 6 15 CoverPerc 15 0.1442308
1607503 Poa trivialis Poa trivialis species Vascular plant 6 45 CoverPerc 45 0.4326923
1607503 Persicaria amphibia Polygonum amphibium species Vascular plant 6 1 CoverPerc 1 0.0096154
1607503 Ranunculus acris Ranunculus acris species Vascular plant 6 1 CoverPerc 1 0.0096154
1607503 Ranunculus repens Ranunculus repens species Vascular plant 6 4 CoverPerc 4 0.0384615
1607503 Rumex acetosa Rumex acetosa species Vascular plant 6 1 CoverPerc 1 0.0096154

Field List

  • PlotObservationID - Plot ID, as in header.
  • Species - Resolved species name, based on taxonomic backbone
  • Species_original - Original species name, as provided by data contributor.
  • Rank_correct - Taxonomic rank at which Species_original was matched.
  • Taxon_group - Possible entries are: Alga_Stonewort, Lichen, Moss, Vascular plant, Unknown.
  • Layer - Vegetation layer, as specified in Turboveg: 0: No layer specified, 1: Upper tree layer, 2: Middle tree layer, 3: Lower tree layer, 4: Upper shrub layer, 5: Lower shrub layer, 6: Herb layer, 7: Juvenile, 8: Seedling, 9: Moss layer.
  • Cover_code - Cover\abundance value in original data, before transformation to percentage cover.
  • Ab_scale - Abundance scale in original data. Possible values are: CoverPerc: Cover Percentage, pa: Presence absence, x_BA: Basal Area, x_IC: Individual count, x_SC: Stem count, x_IV: Relative Importance, x_RF: Relative Frequency.
  • Abundance - Abundance value, in original value, or as transformed from original Cover code to quantitative values.
  • Relative_cover - Abundance of each species after being normalized to 1 in each plot.
  • save(DT2, file = "../_output/DT_sPlot3.0.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=en_US.UTF-8          
    ##  [9] LC_ADDRESS=en_US.UTF-8        LC_TELEPHONE=en_US.UTF-8     
    ## [11] LC_MEASUREMENT=en_US.UTF-8    LC_IDENTIFICATION=en_US.UTF-8
    ## 
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base     
    ## 
    ## other attached packages:
    ##  [1] kableExtra_1.3.1 knitr_1.30       xlsx_0.6.5       forcats_0.5.0   
    ##  [5] stringr_1.4.0    dplyr_1.0.2      purrr_0.3.4      readr_1.4.0     
    ##  [9] tidyr_1.1.2      tibble_3.0.1     ggplot2_3.3.0    tidyverse_1.3.0 
    ## 
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.1.0  xfun_0.19         rJava_0.9-13      haven_2.3.1      
    ##  [5] colorspace_2.0-0  vctrs_0.3.5       generics_0.1.0    viridisLite_0.3.0
    ##  [9] htmltools_0.5.0   yaml_2.2.1        utf8_1.1.4        rlang_0.4.9      
    ## [13] pillar_1.4.3      glue_1.4.2        withr_2.3.0       DBI_1.1.0        
    ## [17] dbplyr_2.0.0      modelr_0.1.6      readxl_1.3.1      lifecycle_0.2.0  
    ## [21] munsell_0.5.0     gtable_0.3.0      cellranger_1.1.0  rvest_0.3.6      
    ## [25] evaluate_0.14     fansi_0.4.1       xlsxjars_0.6.1    highr_0.8        
    ## [29] broom_0.7.0       Rcpp_1.0.5        scales_1.1.1      backports_1.2.0  
    ## [33] webshot_0.5.2     jsonlite_1.7.1    fs_1.5.0          hms_0.5.3        
    ## [37] digest_0.6.25     stringi_1.5.3     grid_3.6.3        cli_2.2.0        
    ## [41] tools_3.6.3       magrittr_2.0.1    crayon_1.3.4      pkgconfig_2.0.3  
    ## [45] ellipsis_0.3.1    xml2_1.3.2        reprex_0.3.0      lubridate_1.7.9.2
    ## [49] assertthat_0.2.1  rmarkdown_2.5     httr_1.4.2        rstudioapi_0.13  
    ## [53] R6_2.5.0          compiler_3.6.3