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$set(echo = TRUE)
library(tidyverse)
library(readr)
library(xlsx)
library(knitr)
library(kableExtra)
#save temporary files
write("TMPDIR = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('TMPDIR'), '.Renviron'))
write("R_USER = /data/sPlot/users/Francesco/_tmp", file=file.path(Sys.getenv('R_USER'), '.Renviron'))
#rasterOptions(tmpdir="/data/sPlot/users/Francesco/_tmp")
Search and replace unclosed quotation marks and escape them. Run in Linux terminal
# escape all double quotation marks. Run in Linux terminal
# sed 's/"/\\"/g' sPlot_3_0_2_species.csv > sPlot_3_0_2_species_test.csv
DT table is the species x plot matrix, in long format.
DT0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_species_test.csv",
delim="\t",
col_type = cols(
PlotObservationID = col_double(),
Taxonomy = col_character(),
`Taxon group` = col_character(),
`Taxon group ID` = col_double(),
`Turboveg2 concept` = col_character(),
`Matched concept` = col_character(),
Match = col_double(),
Layer = col_double(),
`Cover %` = col_double(),
`Cover code` = col_character(),
x_ = col_double()
)
)
nplots <- length(unique(DT0$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 .
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 |
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))
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)
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)
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 |
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
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
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
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.
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 |
PlotObservationID
- Plot ID, as in header
.Species
- Resolved species name, based on taxonomic backboneSpecies_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")
## 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