Nothing
R/msk.macrophytes.2017.prepare.data.R
In ecoval: Procedures for Ecological Assessment of Surface Waters
Defines functions
msk.macrophytes.2017.calc.attrib
msk.macrophytes.2017.compile.sitedat
get.site.sampleid
# ========================================================================================
# This script contains two functions to validate and compile the site and species data
# provided in the templatesas well as a function to calculate the vegetation attributes
# needed for the assessment. The two functions for compiling the site and species data also
# provide initial quality checks for all parameters needed within the tool, and for
# clean-up of the species data axccording to the official taxalist and quality of
# determination.
#
# Required Input as R data frames:
# data.site = Abiotic data collected in sites, user provided in official template
# data.species = Biotic data of taxa collected in sites, user provided in official template
#
# Required Input provided as part of ECOVAL package:
# taxalist.dat = Oficial taxalist
#
# Functions:
# msk.macrophytes.2017.compile.sitedat
# msk.macrophytes.2017.compile.speciesdat
# msk.macrophytes.2017.calc.attrib
#
# Authors:
# Christian Michel and Peter Reichert, EAWAG, Duebendorf, Switzerland
# =======================================================================================
# =======================================
# FUNCTION TO READ AND VALIDATE SITE DATA
# =======================================
get.site.sampleid <- function(siteid, samplingdate) {
fs::path_sanitize(paste(siteid, samplingdate, sep = "_"), replacement = "-")
}
msk.macrophytes.2017.compile.sitedat <- function(data.site,
language = "English",
dictionaries = NA)
{
# get dictionary:
dict <- ecoval.dict(language,dictionaries)
# Create data.frame to collect information on quality checks
site.quality <- matrix(NA, nrow = 0, ncol = 5)
colnames(site.quality) <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("R_macrophytes_error_parameter",dict),
ecoval.translate("R_macrophytes_error_message",dict),
ecoval.translate("R_macrophytes_error_type",dict))
# check if all required columns are contained in data.site
req.colnames <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
#ecoval.translate("A_macrophytes_site_canton",dict),
ecoval.translate("A_macrophytes_site_discharge_lpers",dict),
#ecoval.translate("A_macrophytes_site_discharge_source",dict),
ecoval.translate("A_macrophytes_site_slope_percent",dict),
#ecoval.translate("A_macrophytes_site_slope_source",dict),
ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
ecoval.translate("A_macrophytes_site_shading_percent",dict),
ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_20to40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_6to20_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_2to6_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_0to2_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_sand_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_siltclay_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_mud_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_humuspeat_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_artificial_percent", dict))
ind.req <- match(req.colnames,colnames(data.site))
if ( anyNA(ind.req) )
{
site.quality <- rbind(site.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_sitescolmissing", dict),":",sep=""),
paste(req.colnames[is.na(ind.req)],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
} else {
# Eliminate rows without site and data entry:
data.site <- data.site[!is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]) & !is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),]
if ( nrow(data.site) == 0 )
{
site.quality <- rbind(site.quality,
c("","","",
ecoval.translate("R_macrophytes_error_nositedata", dict),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
}
# Add row names and unique sample identifier column
rnames <- get.site.sampleid(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)])
dup <- duplicated(rnames)
if ( sum(dup) > 0 )
{
site.quality <- rbind(site.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_dupsitedate", dict),":",sep=""),
paste(rnames[dup],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
}
rownames(data.site) <- rnames
data.site <- data.frame(rownames(data.site), data.site, stringsAsFactors = FALSE)
colnames(data.site)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# CHECK IF ALL CORE DATA REQUIRED AS UNIQUE IDENTIFIER OF DATA POINTS IS PRESENT
# SITE CODE
if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_siteid",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))),
rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# CANTON
# if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_canton",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
# DATE
if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_samplingdate",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))),
rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# VALIDATE SITE DATA
# QUALITY CHECK OF OBLIGATORY TYPOLOGY PARAMETERS
# DISCHARGE
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_discharge_lpers",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_discharge_source",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_completesourceofvalue",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
# SLOPE
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_slope_percent",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_slope_source",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_completesourceofvalue",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
if( sum( data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_slope_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# WATER DEPTH
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0m", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)),
rep(ecoval.translate("R_macrophytes_error_valuegt2m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)),
rep(ecoval.translate("R_macrophytes_error_warning",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# SHADING
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)),
rep(ecoval.translate("R_macrophytes_error_valuegt100percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# SUBSTRATE
header.substrat <- c(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_20to40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_6to20_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_2to6_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_0to2_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_sand_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_siltclay_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_mud_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_humuspeat_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_artificial_percent", dict))
# Check substrate size-class data
sum.substrat <- rowSums(data.site[, colnames(data.site) %in% header.substrat], na.rm = T)
if( sum(sum.substrat == 0, na.rm=T) > 0) {
quality.out <- cbind(data.site[sum.substrat == 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[sum.substrat == 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("R_macrophytes_error_substrate", dict), sum(sum.substrat == 0)),
rep(ecoval.translate("R_macrophytes_error_missingdata", dict), sum(sum.substrat == 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),sum(sum.substrat == 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( any(abs(sum.substrat - 100) > 0.9) ) {
quality.out <- cbind(data.site[abs(sum.substrat - 100) > 0.9,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[abs(sum.substrat - 100) > 0.9,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("R_macrophytes_error_substrate", dict), sum(abs(sum.substrat - 100) > 0.9)),
rep(ecoval.translate("R_macrophytes_error_sumnot100%", dict), sum(abs(sum.substrat - 100) > 0.9)),
rep(ecoval.translate("R_macrophytes_error_error", dict), sum(abs(sum.substrat - 100) > 0.9)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# Add proportion of stones (sum substrate size-classes > 6.3 cm) and fine material (< 6.3cm) in data.site
data.site.sub <- data.site[,1:match(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
colnames(data.site)) - 1]
data.site.sub <- data.frame(data.site.sub, rep(NA, nrow(data.site.sub)), rep(NA, nrow(data.site.sub)), stringsAsFactors = FALSE)
colnames(data.site.sub)[ncol(data.site.sub)-1] <- ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)
colnames(data.site.sub)[ncol(data.site.sub)] <- ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)
data.site <- data.frame(data.site.sub, data.site[, match(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
colnames(data.site)):ncol(data.site)])
data.site[,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- rowSums(data.site[, header.substrat[1:3]], na.rm = T)
data.site[,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- rowSums(data.site[, header.substrat[4:9]], na.rm = T)
data.site[,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- NA
for ( i in 1:nrow(data.site) ) {
sum.substrat <- data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] + data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)]
if ( abs(sum.substrat) == 0 ) {
{
if(data.site[i,ecoval.translate("A_macrophytes_site_discharge_lpers", dict)] < 200) {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 39
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- 61
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteil Steine 39% ergaenzt"
} else {
if(data.site[i,ecoval.translate("A_macrophytes_site_discharge_lpers", dict)] < 2000) {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 49
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- 51
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteile Steine 49% ergaenzt"
} else {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 59
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 41
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteile Steine 59% ergaenzt"
}
}
}
} else {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] * (100/sum.substrat)
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] * (100/sum.substrat)
}
}
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = data.site, site.quality = site.quality))
}
}
# ==========================================
# FUNCTION TO READ AND VALIDATE SPECIES DATA
# ==========================================
msk.macrophytes.2017.compile.speciesdat <- function (data.species,
sampling.protocol = "v2018",
language = "English",
dictionaries = NA)
{
# Get dictionary:
dict <- ecoval.dict(language,dictionaries)
# Get taxa list and translate entries to the active language:
taxalist.dat <- ecoval::msk.macrophytes.2017_ListTaxa
for ( j in 1:ncol(taxalist.dat) ) colnames(taxalist.dat)[j] <- ecoval.translate(colnames(taxalist.dat)[j],dict)
col.gf <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict),colnames(taxalist.dat))
if ( !is.na(col.gf) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gf] <- ecoval.translate(taxalist.dat[i,col.gf],dict)
col.gfabbrev <- match(ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),colnames(taxalist.dat))
if ( !is.na(col.gfabbrev) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gfabbrev] <- ecoval.translate(taxalist.dat[i,col.gfabbrev],dict)
col.es <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfo",dict),colnames(taxalist.dat))
if ( !is.na(col.es) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.es] <- ifelse(is.na(taxalist.dat[i,col.es]),NA,ecoval.translate(taxalist.dat[i,col.es],dict))
col.trait <- match(ecoval.translate("A_macrophytes_taxalist_determination_trait",dict),colnames(taxalist.dat))
if ( !is.na(col.trait) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.trait] <- ifelse(is.na(taxalist.dat[i,col.trait]),NA,ecoval.translate(taxalist.dat[i,col.trait],dict))
col.cons <- match(ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict),colnames(taxalist.dat))
if ( !is.na(col.cons) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.cons] <- ifelse(is.na(taxalist.dat[i,col.cons]),NA,ecoval.translate(taxalist.dat[i,col.cons],dict))
# Create data.frame to collect information on quality checks:
species.quality <- matrix(NA, nrow = 0, ncol = 5)
colnames(species.quality) <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("R_macrophytes_error_parameter",dict),
ecoval.translate("R_macrophytes_error_message",dict),
ecoval.translate("R_macrophytes_error_type",dict))
# Check if all required columns are contained in data.species:
req.colnames <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("A_macrophytes_species_number_msk",dict),
ecoval.translate("A_macrophytes_species_name_latin",dict),
ecoval.translate("A_macrophytes_species_absolutecover_percent",dict),
ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_species_determinationuncertainty",dict),
ecoval.translate("A_macrophytes_species_phenology_blossom",dict),
ecoval.translate("A_macrophytes_species_phenology_fruits",dict))
ind.req <- match(req.colnames,colnames(data.species))
if ( anyNA(ind.req) )
{
species.quality <- rbind(species.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_speciescolmissing", dict),":",sep=""),
paste(req.colnames[is.na(ind.req)],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return list with generared outputs
return(list(species.assess = NA,
species.removed = NA,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# Eliminate rows without site and data entry:
data.species <- data.species[!is.na(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)]) & !is.na(data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),]
if ( nrow(data.species) == 0 )
{
species.quality <- rbind(species.quality,
c("","","",
ecoval.translate("R_macrophytes_error_nospeciesdata", dict),
ecoval.translate("R_macrophytes_error_error", dict)))
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(species.assess = NA,
species.removed = NA,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# Add unique identifier for individual assessments in extra column; and species within assessments as row names:
data.species <- data.frame(get.site.sampleid(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)],
data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
data.species, stringsAsFactors = FALSE)
colnames(data.species)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# Function to generate output for quality checks
split.last.underline <- function(x)
{
y <- strsplit(x,split="_")[[1]]
n <- length(y)
x2 <- y[n]
x1 <- NA
if ( n == 2 ) x1 <- y[1]
if ( n > 2 ) x1 <- paste(y[1:(n-1)],collapse="_")
return(c(x1,x2))
}
# Perform quality checks
# CHECK IF ALL CORE DATA REQUIRED AS UNIQUE IDENTIFIER ARE PRESENT
# # SITE CODE # *** note: lines without site code are now eliminated above
#
# ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)]) # site code NA
# if ( sum(ind.na) > 0 ) {
# quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
# quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_site_siteid",dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
# colnames(quality.out) <- colnames(species.quality)
# species.quality <- rbind(species.quality, quality.out)
# }
#
# # DATE # *** note: lines without data are now eliminated above
#
# ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]) # sampling data NA
# if ( sum(ind.na) > 0 ) {
# quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
# quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_site_samplingdate",dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
# colnames(quality.out) <- colnames(species.quality)
# species.quality <- rbind(species.quality, quality.out)
# }
# SPECIES NUMBER
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)]) # species taxa list number NA
if( sum(ind.na) > 0 ) {
quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_number_msk",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# VALIDATE SPECIES DATA
# QUALITY CHECK OF OBLIGATORY PARAMETERS
# ABSOLUTE COVER OF INDIVIDUAL SPECIES
ind.taxalist <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)]
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
ind.bry <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
# Check if cover values are given for each species except bryophytes and characea
ind.illegal.na <- ind.na & !ind.bry & !ind.cha & ind.taxalist
if( sum( ind.illegal.na ) > 0 ) {
#quality.out <- unique(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
site_sample_species <- aggregate(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_species_number_msk",dict)],
by=list(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_site_sampleid",dict)]),FUN=paste,collapse=",")
quality.out <- site_sample_species[,1]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, paste(rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)),":",site_sample_species[,2]))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if 0% cover values are present, which is either not correct or species should be not contained in data
ind.zero <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] == 0; ind.zero[is.na(ind.zero)] <- FALSE
if( sum(ind.zero) > 0 ) {
quality.out <- unique(data.species[ind.zero,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_value0percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if <0% cover values are present
ind.ltzero <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] < 0; ind.ltzero[is.na(ind.ltzero)] <- FALSE
if( sum(ind.ltzero) > 0 ) {
quality.out <- unique(data.species[ind.ltzero,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if >100% cover values are present
ind.gt100 <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] >= 100; ind.gt100[is.na(ind.gt100)] <- FALSE
if( sum(ind.gt100) > 0 ) {
quality.out <- unique(data.species[ind.gt100,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_valuegt100percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check consistency of sum bry and sum cha with brysum and chasum.
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("",length(Erhebung_Code))
for ( i in 1:length(dg.abs.check) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
if ( sum(ind.brysum&ind.sampling) > 0 & sum(ind.bry&ind.sampling) > 0 )
{
dg.brysum <- sum(data.species[ind.brysum&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.bry <- sum(data.species[ind.bry &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.bryall <- sum(data.species[ind.bry &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=FALSE)
if ( !is.na(dg.brysum) )
{
if ( !is.na(dg.bryall) ) # all species coverages available
{
if( abs(dg.brysum-dg.bryall) > 0.001 ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry",sep="")
}
else
{
if ( !is.na(dg.bry) ) # some species coverages available
{
if( dg.bry > dg.brysum ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry",sep="")
}
}
}
}
if ( sum(ind.chasum&ind.sampling) > 0 & sum(ind.cha&ind.sampling) > 0 )
{
dg.chasum <- sum(data.species[ind.chasum&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.cha <- sum(data.species[ind.cha &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.chaall <- sum(data.species[ind.cha &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=FALSE)
if ( !is.na(dg.chasum) )
{
if ( !is.na(dg.chaall) ) # all species coverages available
{
if( abs(dg.chasum-dg.chaall) > 0.001 ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha",sep="")
}
else
{
if ( !is.na(dg.cha) ) # some species coverages available
{
if( dg.cha > dg.chasum ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha",sep="")
}
}
}
}
}
if( sum(dg.abs.check == "bry", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[substring(dg.abs.check,1,3) == "bry"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumbryincompatible", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
if( sum(dg.abs.check == "cha",na.rm=T) > 0 | sum(dg.abs.check == "brycha", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "cha" | dg.abs.check == "brycha"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumchaincompatible", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check sum of cover values is > 100%.
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("ok",length(Erhebung_Code))
for ( i in 1:length(dg.abs.check) ) {
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
ind.test <- ind.sampling
if ( sum(ind.brysum&ind.sampling) > 0 ) ind.test <- ind.test & !ind.bry # if summary species present, exclude individual taxa
if ( sum(ind.chasum&ind.sampling) > 0 ) ind.test <- ind.test & !ind.cha # if summary species present, exclude individual taxa
if ( sum(data.species[ind.test,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE) > 100 )
{
dg.abs.check[i] <- "error"
}
}
if( sum(dg.abs.check == "error", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "error"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumabscoveragegt100%", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if uncertainty of determination is given for each species, only if sampling protocol 2018 was used
if ( sampling.protocol == "v2018" ) {
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)])
if( sum( ind.na ) > 0 ) {
site_sample_species <- aggregate(data.species[ind.na,ecoval.translate("A_macrophytes_species_number_msk",dict)],
by=list(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)]),FUN=paste,collapse=",")
quality.out <- site_sample_species[,1]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_determinationuncertainty",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, paste(rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)),":",site_sample_species[,2]))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
}
## Reduce data-set to species on taxalist
species.not.taxalist <- data.species[data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] == FALSE,]
data.species <- data.species[data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)],]
# Add column for comments regarding species validation (determination quality, see below)
data.species[, ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- NA
# Add growthform information to data
names <- colnames(data.species)
data.species <- cbind(data.species,NA,NA)
colnames(data.species) <- c(names,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict))
for( i in 1:nrow(data.species) ) {
row.taxalist <- match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
if( !is.na(row.taxalist) ) {
data.species[i, ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i, ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
}
}
# Check for double entries (only for taxa in taxa list).
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
spec.num.check <- rep("ok",length(Erhebung_Code))
for ( i in 1:length(Erhebung_Code) ) {
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
spec.number <- data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)]
if ( length(spec.number) != length(unique(spec.number))) spec.num.check[i] <- "error"
}
if( sum(spec.num.check == "error", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[spec.num.check == "error"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_number_msk",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_doubleentries", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check species assignments and determination accuracy, and assign them to aggregates if this is adequate
# This is only done for data collected with sampling protocol published 2017/2018
# For old data species are taken as they are and determination uncertanity is not considered
rows.delete <- rep(FALSE,nrow(data.species))
if ( sampling.protocol == "v2018" ) {
for ( i in 1:nrow(data.species) ) {
row.taxalist <- match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[ ,ecoval.translate("A_macrophytes_species_number_msk",dict)])
detunc.data.species <- data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)]
if( !is.na(detunc.data.species) ) {
# Get corresponding determination value and taxa-group assigment from taxalist
detval.taxalist.dat <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_determination",dict)]
grouptaxon.taxalist.dat <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_taxagroups_assign",dict)]
# Do quality check for bryophyte species
if( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] == ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict) ) {
if ( ( detval.taxalist.dat <= 2 & detunc.data.species == 4 ) |
( detval.taxalist.dat == 3 & detunc.data.species != 2 ) ) {
if( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
} else {
rows.delete[i] <- TRUE
}
}
} else { # end if bryophyte
# Quality check for all higher macrophytes species
if ( detval.taxalist.dat <= 2 ) { # detval <= 2
if( detunc.data.species == 4 ) { # detunc = 4
if( !is.na(grouptaxon.taxalist.dat) ) { # aggregate tayon exists
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else { # no aggregate taxon
rows.delete[i] <- TRUE
}
} # end detunc = 4
} else { # detval > 2
if ( detval.taxalist.dat == 3 ) {
# For species with a determination value of 3 in the taxalist check if phenology trait necessary for correct
# determination of the taxon was present
phen.check.code <- taxalist.dat[match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[ ,ecoval.translate("A_macrophytes_species_number_msk",dict)]),
ecoval.translate("A_macrophytes_taxalist_phenology_trait",dict)]
phen.check.ok <- FALSE
# Code 1 for species in taxalist: Blossoms need to be present
if( phen.check.code == 1 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
} else {
# Code 2 for species in taxalist: Fruits need to be present
if( phen.check.code == 2 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)])
} else {
# Code 3 for species in taxalist: Blossoms OR fruits need to be present
if( phen.check.code == 3 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)]) | !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
} else {
# Code 3 for species in taxalist: Blossoms AND fruits need to be present
if( phen.check.code == 4 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)]) & !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
}
}
}
}
# Species is corrected to group-taxon if required phenology trait was not present or if determination was insecure
if ( !phen.check.ok | detunc.data.species > 3 ) {
if ( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else {
rows.delete[i] <- TRUE
}
}
} else {
if ( detval.taxalist.dat == 4 ) {
if ( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else {
rows.delete[i] <- TRUE
}
}
}
}
} # end not bryophyte
} # end if( !is.na(detunc.data.species) )
} # end for ( i in 1:nrow(data.species) )
} # end if ( sampling.protocol == "v2018" )
# Correct overall bryophyta and characea coverage:
if ( sum(rows.delete) > 0 )
{
for ( i in which(rows.delete) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] ==
data.species[i,ecoval.translate("A_macrophytes_site_sampleid",dict)]
# bryophytes:
if ( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict) )
{
if ( !is.na(data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) )
{
ind.brysum <- which(ind.sampling &
data.species[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict) )
if ( length(ind.brysum) == 1 )
{
data.species[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] =
data.species[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] -
data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
}
}
}
# characea:
if ( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict) )
{
if ( !is.na(data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) )
{
ind.chasum <- which(ind.sampling &
data.species[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict) )
if ( length(ind.chasum) == 1 )
{
data.species[ind.chasum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] =
data.species[ind.chasum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] -
data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
}
}
}
}
}
# Remove species that did not conform to determination quality requirements
species.insuff.determ. <- data.species[rows.delete,]
data.species <- data.species[!rows.delete,]
# Aggregate douplicated species (can occur due to aggregation made above), aggregate algae,
# and add aggregate taxon for bryophytes and characea if needed:
ind.bry <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.delete <- rep(FALSE,nrow(data.species))
rows.add <- data.species[0,]
sampling.unique <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("",length(sampling.unique))
for ( i in 1:length(sampling.unique) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == sampling.unique[i]
ind.samp <- which(ind.sampling)
if ( length(ind.samp) > 0 )
{
# replace algae species by aggregate:
ind <- ind.samp[data.species[ind.sampling,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
if ( length(ind) > 0 )
{
for ( j in ind )
{
row.taxalist <- which(taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] ==
data.species[j,ecoval.translate("A_macrophytes_species_number_msk",dict)])
taxagroup.assign <- taxalist.dat[row.taxalist,ecoval.translate("A_macrophytes_taxalist_taxagroups_assign",dict)]
if ( !is.na(taxagroup.assign) )
{
data.species[j,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxagroup.assign
data.species[j,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[which(taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] == taxagroup.assign),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
}
}
}
# add aggregate taxa for bryophytes and characea if needed:
if ( sum(ind.bry&ind.sampling) > 0 & sum(ind.brysum&ind.sampling)==0 )
{
rows.add <- rbind(rows.add,rep(NA,ncol(rows.add)))
colnames(rows.add) <- colnames(data.species)
ind.add <- nrow(rows.add)
ind.copy <- which(ind.bry&ind.sampling)[1]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_siteid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_siteid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_samplingdate",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_samplingdate",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_sampleid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_sampleid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_name_latin",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)] <-
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]*
data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)])/
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- 1
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_assess_bryophyte",dict)
if ( is.na(rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry")
}
if ( sum(ind.cha&ind.sampling) > 0 & sum(ind.chasum&ind.sampling)==0 )
{
rows.add <- rbind(rows.add,rep(NA,ncol(rows.add)))
colnames(rows.add) <- colnames(data.species)
ind.add <- nrow(rows.add)
ind.copy <- which(ind.cha&ind.sampling)[1]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_siteid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_siteid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_samplingdate",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_samplingdate",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_sampleid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_sampleid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_name_latin",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)] <-
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]*
data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)])/
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- 1
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)
if ( is.na(rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha")
}
# aggregate identical taxa:
species_no <- unique(data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)])
for ( j in 1:length(species_no) )
{
ind <- ind.samp[data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)] == species_no[j]]
if ( length(ind) > 1 )
{
data.species[ind[1],ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.delete[ind[2:length(ind)]] <- TRUE
}
}
}
}
data.species <- data.species[!rows.delete,]
if ( nrow(rows.add) > 0 ) data.species <- rbind(data.species,rows.add)
if( sum(dg.abs.check == "bry",na.rm=T) > 0 ) {
quality.out <- Erhebung_Code[substring(dg.abs.check,1,3) == "bry"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_abscoveragebrymissing", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
if( sum(dg.abs.check == "cha",na.rm=T)>0 | sum(dg.abs.check == "brycha",na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "cha" | dg.abs.check == "brycha"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_abscoveragechamissing", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Combine all data containing removed species, either due to taxalist or insufficient determination, in one data.frame
species.insuff.determ.[,ecoval.translate("A_macrophytes_species_reason_elimination",dict)] <- rep(ecoval.translate("A_macrophytes_species_reason_insufficientquality",dict), nrow(species.insuff.determ.))
species.not.taxalist[,ecoval.translate("A_macrophytes_species_reason_elimination",dict)] <- rep(ecoval.translate("A_macrophytes_species_reason_notontaxalist",dict), nrow(species.not.taxalist))
# Remove additional columns in species.insuff.determ. introduced during data perparation after reductioh to species not on taxalist
species.insuff.determ. <- species.insuff.determ.[,colnames(species.insuff.determ.) %in% colnames(species.not.taxalist)]
# Create output data.frame with all removed species
species.removed <- rbind(species.insuff.determ., species.not.taxalist, stringsAsFactors = FALSE)
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return list with generated outputs
return(list(species.assess = data.species,
species.removed = species.removed,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# ================================
# FUNCTION TO CALCULATE ATTRIBUTES
# ================================
msk.macrophytes.2017.calc.attrib <- function(data.site,
data.species,
language = "English",
dictionaries = NA)
{
# get dictionary:
dict <- ecoval.dict(language,dictionaries)
# get taxa list:
taxalist.dat <- ecoval::msk.macrophytes.2017_ListTaxa
for ( j in 1:ncol(taxalist.dat) ) colnames(taxalist.dat)[j] <- ecoval.translate(colnames(taxalist.dat)[j],dict)
col.gf <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict),colnames(taxalist.dat))
if ( !is.na(col.gf) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gf] <- ecoval.translate(taxalist.dat[i,col.gf],dict)
col.gfabbrev <- match(ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),colnames(taxalist.dat))
if ( !is.na(col.gfabbrev) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gfabbrev] <- ecoval.translate(taxalist.dat[i,col.gfabbrev],dict)
col.es <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfo",dict),colnames(taxalist.dat))
if ( !is.na(col.es) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.es] <- ifelse(is.na(taxalist.dat[i,col.es]),NA,ecoval.translate(taxalist.dat[i,col.es],dict))
col.trait <- match(ecoval.translate("A_macrophytes_taxalist_determination_trait",dict),colnames(taxalist.dat))
if ( !is.na(col.trait) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.trait] <- ifelse(is.na(taxalist.dat[i,col.trait]),NA,ecoval.translate(taxalist.dat[i,col.trait],dict))
col.cons <- match(ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict),colnames(taxalist.dat))
if ( !is.na(col.cons) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.cons] <- ifelse(is.na(taxalist.dat[i,col.cons]),NA,ecoval.translate(taxalist.dat[i,col.cons],dict))
# Create data.frame to collect calculated attributes
attrib.names <- c(ecoval.translate("A_macrophytes_taxa_all_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_all_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict),
ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict),
ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict),
ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict),
ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict),
ecoval.translate("A_macrophytes_priorityspecies1_count",dict),
ecoval.translate("A_macrophytes_priorityspecies2_count",dict),
ecoval.translate("A_macrophytes_priorityspecies3_count",dict),
ecoval.translate("A_macrophytes_priorityspecies4_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic",dict),
ecoval.translate("A_macrophytes_taxa_helophytes",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes",dict))
attrib.dat <- data.frame(matrix(NA, nrow = nrow(data.site), ncol = length(attrib.names)), stringsAsFactors = FALSE)
colnames(attrib.dat) <- attrib.names
rownames(attrib.dat) <- rownames(data.site)
# Fill data.frame with entries as if no vegetation is present at sites. For sites with vegetation cover these values will be
# later replaced by calculated attribute values
attrib.dat[, c(ecoval.translate("A_macrophytes_taxa_all_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_all_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict),
ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict),
ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict))] <- 0
# Calculate attributes for all data contained in data.site
for ( i in 1:nrow(attrib.dat) ) {
# Create data.frame with vegetation data of sampling i:
data.select <- data.species[data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == rownames(attrib.dat)[i],
c(ecoval.translate("A_macrophytes_species_name_latin",dict),
ecoval.translate("A_macrophytes_species_number_msk",dict),
ecoval.translate("A_macrophytes_species_absolutecover_percent", dict),
ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict))]
# get indices of bryophytes and characea:
ind.bry <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
ind.alg <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
ind.helo <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_helophyte",dict)]
ind.aquatic <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)]
if( nrow(data.select) > 0 ) {
## ABSOLUTE COVER / BIOMASS ATTRIBUTES
# Absolute cover of all vegetation at site
attrib.dat[i, ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict)] <-
sum(data.select[!(ind.bry|ind.cha),ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
# Calculate and add relative cover per species (Colname: "Rel_Deckung") in selected vegetation data
data.select$Rel_Deckung <- data.select[, ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] * 100 /
attrib.dat[i, ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict)]
# Absolute cover of only higher macrophytes
attrib.dat[i, ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict)] <-
sum(data.select[!(ind.bry|ind.cha|ind.alg),ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
# Add relevant information from taxalist to data-set
data.select$neophyte_info <- rep(NA, nrow(data.select))
data.select$priority <- rep(NA, nrow(data.select))
data.select$guide_value <- rep(NA, nrow(data.select))
for (j in 1:nrow(data.select)) {
row.taxalist <- match(data.select[j,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
data.select[j, "neophyte_info"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict)]
data.select[j, "priority"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_priorityspecies",dict)]
data.select[j, "guide_value"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_indexspecies",dict)]
}
# Generate data.frame for calculation of richness and dominance by bundling species that can be emerged or submerged
data.select.richness <- data.select
rows.delete <- rep(FALSE,nrow(data.select.richness))
for ( j in 1:nrow(data.select.richness) )
{
row.taxalist <- match(data.select.richness[j,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
number.msk.grfoval <- taxalist.dat[row.taxalist,ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfoval",dict)]
if ( !is.na(number.msk.grfoval) )
{
row.data.select <- match(number.msk.grfoval,data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
if ( !is.na(row.data.select) )
{
# add absolute coverage from row that should be eliminated:
data.select.richness[row.data.select,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
data.select.richness[row.data.select,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] +
data.select.richness[j,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
# add absolute coverage from row that should be eliminated:
data.select.richness[row.data.select,"Rel_Deckung"] <-
data.select.richness[row.data.select,"Rel_Deckung"] +
data.select.richness[j,"Rel_Deckung"]
# mark row for deletion:
rows.delete[j] <- TRUE
}
}
}
if ( sum(rows.delete) > 0 ) data.select.richness <- data.select.richness[!rows.delete,]
# get indices of bryophytes and characea:
ind.richness.bry <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.richness.brysum <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.richness.cha <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.richness.chasum <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
ind.richness.alg <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
ind.richness.helo <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_helophyte",dict)]
ind.richness.aquatic <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)]
# # Generate data.frames with only data on higher macrophytes (i.e. no algae)
# dat.makro <- data.select[data.select[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] != ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict),]
# dat.makro.richness <- data.select.richness[data.select.richness[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] != ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict),]
## DIVERSITY ATTRIBUTES
# SPECIES RICHNESS
# number of summary taxa and algae:
num.bry <- sum(ind.richness.bry); if( num.bry==0 & sum(ind.richness.brysum)>0 ) num.bry <- 1
num.aquatic <- sum(ind.richness.aquatic) - sum(ind.richness.chasum); if ( sum(ind.richness.cha)==0 & sum(ind.richness.chasum)>0 ) num.aquatic <- num.aquatic+1
num.helo <- sum(ind.richness.helo)
num.alg <- sum(ind.richness.alg)
# Species richness of all growthforms, without algae:
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_all_richness_count",dict)] <- num.bry + num.aquatic + num.helo
# Species richness per growthform group (i.e. aquatic, helophyte, bryophyte)
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict)] <- num.aquatic
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict)] <- num.helo
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict)] <- num.bry
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict)] <-
sum(data.select.richness[!(ind.richness.brysum|ind.richness.chasum|ind.richness.alg),"neophyte_info"] == "N",na.rm=T)
# GROWTHFORM RICHNESS
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_all_richness_count",dict)] <-
length(na.omit(unique(data.select[!(ind.brysum|ind.chasum|ind.alg),ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict)] <-
length(na.omit(unique(data.select[ind.helo,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict)] <-
length(na.omit(unique(data.select[ind.aquatic&(!ind.chasum),ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
## COMMUNITY COMPOSITION
# HEIPs DOMINANCE INDEX (calculated with data.select.richness)
#dat.rel.sw <- data.select.richness[!(ind.richness.bry|ind.richness.cha|ind.richness.alg), "Rel_Deckung"]
dat.rel.sw <- data.select.richness[!(ind.richness.bry|ind.richness.cha), "Rel_Deckung"] # previous implementation was with algae
n.taxa.sw <- length(na.omit(dat.rel.sw))
sw.index.bry <- -(sum((dat.rel.sw/100) * log((dat.rel.sw/100))))
if ( n.taxa.sw == 1 ) sw.index.bry <- 0
if ( n.taxa.sw == 0 ) sw.index.bry <- NA
attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- (exp(sw.index.bry) - 1) / (n.taxa.sw - 1) #ATTRIBUT
if ( n.taxa.sw == 1 ) attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- 0 # Minimaler Wert fuer Heip Index ist 0.006 (cf. Maggurran A., Measuring biological diversity )
if ( n.taxa.sw == 0 ) attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- NA
rm(list = c("dat.rel.sw", "n.taxa.sw", "sw.index.bry"))
# NEOPHYTES RELATIVE COVER
ind.neo <- data.select[, "neophyte_info"] == "N"; ind.neo <- ifelse(is.na(ind.neo),FALSE,ind.neo)
attrib.dat[i,ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict)] <-
sum(data.select[ind.neo , "Rel_Deckung"]) #ATTRIBUT
# RELATIVE COVER OF GROWTHFORMS (i.e. AQUATIC, HELOPHYTES, and BRYOPHYTES)
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] <-
sum(data.select[ind.aquatic&!ind.cha,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] <-
sum(data.select[ind.helo,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] <-
sum(data.select[ind.brysum,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] <-
sum(data.select[ind.alg,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] <- 100
}
# ADJUST RELATIVE COVER BRYOPHYTES TO PROPORTION ON ARTIFICIAL SUBSTRATE FOR ASSESSMENT
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <- NA
if ( !is.na(attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)]) )
{
if( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] > 0 ) {
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)] <-
data.select[ind.brysum,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent", dict)] # Attribute for illustrative purposes in value function
if( is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]) ) {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <-
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] #ATTRIBUT
} else {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <-
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] *
(1 - attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]/100) +
0.33 * attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] *
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]/100 #ATTRIBUT
}
} else {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <- 0
}
}
# ABSOLUTE COVER BY GROWTHFORMS, i.e. AQUATIC, HELOPHYTES, and BRYOPHYTES
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] <-
sum(data.select[ind.aquatic&!ind.cha,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] <-
sum(data.select[ind.helo,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] <-
sum(data.select[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] <-
sum(data.select[ind.alg,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) #ATTRIBUT
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] <- 100
}
## SPECIES QUALITY / CONSERVAT
# PRIORITY SPECIES
# NUMBER OF SPECIES FOR EACH PRIORITY CATEGORY
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 1, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 2, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 3, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 4, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)] <- 0
}
# GUIDE VALUES, NUMBER OF SPECIES ("Leitarten")
# Anzahl Arten mit spezifischem Leitwert
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 1, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 2, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 3, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)] <- 0
}
# BRYOPHYTES
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 1, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)] <- 0
}
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 2, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)] <- 0
}
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 3, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)] <- 0
}
## Collect taxon names grouped by growthform ZH
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic",dict)] <-
paste(data.select[ind.aquatic&!ind.chasum,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes",dict)] <-
paste(data.select[ind.helo,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes",dict)] <-
paste(data.select[ind.bry,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
rm(list = c("data.select"))
}
}
# Return results
# Data tables as tab-delimited text-files to output directiory
# For this output the sampling event code is written in the first column
# attrib.out <- data.frame(rownames(attrib.dat), attrib.dat, stringsAsFactors = FALSE)
# colnames(attrib.out)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# write.table(attrib.out, paste(OutputDir,"AttributeData_Final.dat", sep = ""), quote = FALSE, row.names = FALSE, na = "", sep = "\t")
# Generated data.frame to environment
return(attrib.dat)
}
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Defines functions msk.macrophytes.2017.calc.attrib msk.macrophytes.2017.compile.sitedat get.site.sampleid
# ========================================================================================
# This script contains two functions to validate and compile the site and species data
# provided in the templatesas well as a function to calculate the vegetation attributes
# needed for the assessment. The two functions for compiling the site and species data also
# provide initial quality checks for all parameters needed within the tool, and for
# clean-up of the species data axccording to the official taxalist and quality of
# determination.
#
# Required Input as R data frames:
# data.site = Abiotic data collected in sites, user provided in official template
# data.species = Biotic data of taxa collected in sites, user provided in official template
#
# Required Input provided as part of ECOVAL package:
# taxalist.dat = Oficial taxalist
#
# Functions:
# msk.macrophytes.2017.compile.sitedat
# msk.macrophytes.2017.compile.speciesdat
# msk.macrophytes.2017.calc.attrib
#
# Authors:
# Christian Michel and Peter Reichert, EAWAG, Duebendorf, Switzerland
# =======================================================================================
# =======================================
# FUNCTION TO READ AND VALIDATE SITE DATA
# =======================================
get.site.sampleid <- function(siteid, samplingdate) {
fs::path_sanitize(paste(siteid, samplingdate, sep = "_"), replacement = "-")
}
msk.macrophytes.2017.compile.sitedat <- function(data.site,
language = "English",
dictionaries = NA)
{
# get dictionary:
dict <- ecoval.dict(language,dictionaries)
# Create data.frame to collect information on quality checks
site.quality <- matrix(NA, nrow = 0, ncol = 5)
colnames(site.quality) <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("R_macrophytes_error_parameter",dict),
ecoval.translate("R_macrophytes_error_message",dict),
ecoval.translate("R_macrophytes_error_type",dict))
# check if all required columns are contained in data.site
req.colnames <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
#ecoval.translate("A_macrophytes_site_canton",dict),
ecoval.translate("A_macrophytes_site_discharge_lpers",dict),
#ecoval.translate("A_macrophytes_site_discharge_source",dict),
ecoval.translate("A_macrophytes_site_slope_percent",dict),
#ecoval.translate("A_macrophytes_site_slope_source",dict),
ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
ecoval.translate("A_macrophytes_site_shading_percent",dict),
ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_20to40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_6to20_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_2to6_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_0to2_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_sand_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_siltclay_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_mud_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_humuspeat_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_artificial_percent", dict))
ind.req <- match(req.colnames,colnames(data.site))
if ( anyNA(ind.req) )
{
site.quality <- rbind(site.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_sitescolmissing", dict),":",sep=""),
paste(req.colnames[is.na(ind.req)],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
} else {
# Eliminate rows without site and data entry:
data.site <- data.site[!is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]) & !is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),]
if ( nrow(data.site) == 0 )
{
site.quality <- rbind(site.quality,
c("","","",
ecoval.translate("R_macrophytes_error_nositedata", dict),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
}
# Add row names and unique sample identifier column
rnames <- get.site.sampleid(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)])
dup <- duplicated(rnames)
if ( sum(dup) > 0 )
{
site.quality <- rbind(site.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_dupsitedate", dict),":",sep=""),
paste(rnames[dup],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = NA,
site.quality = site.quality))
}
rownames(data.site) <- rnames
data.site <- data.frame(rownames(data.site), data.site, stringsAsFactors = FALSE)
colnames(data.site)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# CHECK IF ALL CORE DATA REQUIRED AS UNIQUE IDENTIFIER OF DATA POINTS IS PRESENT
# SITE CODE
if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_siteid",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))),
rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_siteid",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# CANTON
# if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_canton",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_canton",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
# DATE
if ( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_samplingdate",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))),
rep(ecoval.translate("R_macrophytes_error_incompleterecord",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# VALIDATE SITE DATA
# QUALITY CHECK OF OBLIGATORY TYPOLOGY PARAMETERS
# DISCHARGE
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_discharge_lpers",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_lpers",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_discharge_source",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_completesourceofvalue",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_discharge_source",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
# SLOPE
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_slope_percent",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]) ) > 0 ) {
# quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]),
# ecoval.translate("A_macrophytes_site_siteid",dict)],
# data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]),
# ecoval.translate("A_macrophytes_site_samplingdate",dict)],
# rep(ecoval.translate("A_macrophytes_site_slope_source",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_completesourceofvalue",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))),
# rep(ecoval.translate("R_macrophytes_error_warning",dict),
# sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_slope_source",dict)]))))
# colnames(quality.out) <- colnames(site.quality)
# site.quality <- rbind(site.quality, quality.out)
# }
if( sum( data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_slope_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_slope_percent",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# WATER DEPTH
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0m", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_waterdepth_m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)),
rep(ecoval.translate("R_macrophytes_error_valuegt2m",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)),
rep(ecoval.translate("R_macrophytes_error_warning",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_waterdepth_m",dict)] > 2)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# SHADING
if( sum( is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]) ) > 0 ) {
quality.out <- cbind(data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]),
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]),
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_completevalue", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(is.na(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)]))))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] < 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( sum( data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100 , na.rm=T) > 0 ) {
quality.out <- cbind(data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("A_macrophytes_site_shading_percent",dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)),
rep(ecoval.translate("R_macrophytes_error_valuegt100percent", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)),
rep(ecoval.translate("R_macrophytes_error_error", dict),
sum(data.site[,ecoval.translate("A_macrophytes_site_shading_percent",dict)] > 100)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# SUBSTRATE
header.substrat <- c(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_20to40_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_6to20_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_2to6_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_0to2_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_sand_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_siltclay_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_mud_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_humuspeat_percent", dict),
ecoval.translate("A_macrophytes_site_substrate_artificial_percent", dict))
# Check substrate size-class data
sum.substrat <- rowSums(data.site[, colnames(data.site) %in% header.substrat], na.rm = T)
if( sum(sum.substrat == 0, na.rm=T) > 0) {
quality.out <- cbind(data.site[sum.substrat == 0,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[sum.substrat == 0,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("R_macrophytes_error_substrate", dict), sum(sum.substrat == 0)),
rep(ecoval.translate("R_macrophytes_error_missingdata", dict), sum(sum.substrat == 0)),
rep(ecoval.translate("R_macrophytes_error_error", dict),sum(sum.substrat == 0)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
if( any(abs(sum.substrat - 100) > 0.9) ) {
quality.out <- cbind(data.site[abs(sum.substrat - 100) > 0.9,
ecoval.translate("A_macrophytes_site_siteid",dict)],
data.site[abs(sum.substrat - 100) > 0.9,
ecoval.translate("A_macrophytes_site_samplingdate",dict)],
rep(ecoval.translate("R_macrophytes_error_substrate", dict), sum(abs(sum.substrat - 100) > 0.9)),
rep(ecoval.translate("R_macrophytes_error_sumnot100%", dict), sum(abs(sum.substrat - 100) > 0.9)),
rep(ecoval.translate("R_macrophytes_error_error", dict), sum(abs(sum.substrat - 100) > 0.9)))
colnames(quality.out) <- colnames(site.quality)
site.quality <- rbind(site.quality, quality.out)
}
# Add proportion of stones (sum substrate size-classes > 6.3 cm) and fine material (< 6.3cm) in data.site
data.site.sub <- data.site[,1:match(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
colnames(data.site)) - 1]
data.site.sub <- data.frame(data.site.sub, rep(NA, nrow(data.site.sub)), rep(NA, nrow(data.site.sub)), stringsAsFactors = FALSE)
colnames(data.site.sub)[ncol(data.site.sub)-1] <- ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)
colnames(data.site.sub)[ncol(data.site.sub)] <- ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)
data.site <- data.frame(data.site.sub, data.site[, match(ecoval.translate("A_macrophytes_site_substrate_above40_percent", dict),
colnames(data.site)):ncol(data.site)])
data.site[,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- rowSums(data.site[, header.substrat[1:3]], na.rm = T)
data.site[,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- rowSums(data.site[, header.substrat[4:9]], na.rm = T)
data.site[,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- NA
for ( i in 1:nrow(data.site) ) {
sum.substrat <- data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] + data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)]
if ( abs(sum.substrat) == 0 ) {
{
if(data.site[i,ecoval.translate("A_macrophytes_site_discharge_lpers", dict)] < 200) {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 39
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- 61
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteil Steine 39% ergaenzt"
} else {
if(data.site[i,ecoval.translate("A_macrophytes_site_discharge_lpers", dict)] < 2000) {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 49
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- 51
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteile Steine 49% ergaenzt"
} else {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 59
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- 41
data.site[i,ecoval.translate("A_macrophytes_site_sumsubstrate_comment",dict)] <- "Nur kuenstliches Substrat, Anteile Steine 59% ergaenzt"
}
}
}
} else {
data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] <- data.site[i,ecoval.translate("A_macrophytes_site_coarsegravel_percent",dict)] * (100/sum.substrat)
data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] <- data.site[i,ecoval.translate("A_macrophytes_site_finesubstrate_percent",dict)] * (100/sum.substrat)
}
}
# ERGAENZT: Complement "site.quality" data.frame with data-set information
site.quality <- cbind(site.quality, rep(ecoval.translate("R_macrophytes_error_sitedate", dict), nrow(site.quality)))
colnames(site.quality)[ncol(site.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(data.site = data.site, site.quality = site.quality))
}
}
# ==========================================
# FUNCTION TO READ AND VALIDATE SPECIES DATA
# ==========================================
msk.macrophytes.2017.compile.speciesdat <- function (data.species,
sampling.protocol = "v2018",
language = "English",
dictionaries = NA)
{
# Get dictionary:
dict <- ecoval.dict(language,dictionaries)
# Get taxa list and translate entries to the active language:
taxalist.dat <- ecoval::msk.macrophytes.2017_ListTaxa
for ( j in 1:ncol(taxalist.dat) ) colnames(taxalist.dat)[j] <- ecoval.translate(colnames(taxalist.dat)[j],dict)
col.gf <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict),colnames(taxalist.dat))
if ( !is.na(col.gf) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gf] <- ecoval.translate(taxalist.dat[i,col.gf],dict)
col.gfabbrev <- match(ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),colnames(taxalist.dat))
if ( !is.na(col.gfabbrev) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gfabbrev] <- ecoval.translate(taxalist.dat[i,col.gfabbrev],dict)
col.es <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfo",dict),colnames(taxalist.dat))
if ( !is.na(col.es) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.es] <- ifelse(is.na(taxalist.dat[i,col.es]),NA,ecoval.translate(taxalist.dat[i,col.es],dict))
col.trait <- match(ecoval.translate("A_macrophytes_taxalist_determination_trait",dict),colnames(taxalist.dat))
if ( !is.na(col.trait) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.trait] <- ifelse(is.na(taxalist.dat[i,col.trait]),NA,ecoval.translate(taxalist.dat[i,col.trait],dict))
col.cons <- match(ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict),colnames(taxalist.dat))
if ( !is.na(col.cons) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.cons] <- ifelse(is.na(taxalist.dat[i,col.cons]),NA,ecoval.translate(taxalist.dat[i,col.cons],dict))
# Create data.frame to collect information on quality checks:
species.quality <- matrix(NA, nrow = 0, ncol = 5)
colnames(species.quality) <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("R_macrophytes_error_parameter",dict),
ecoval.translate("R_macrophytes_error_message",dict),
ecoval.translate("R_macrophytes_error_type",dict))
# Check if all required columns are contained in data.species:
req.colnames <- c(ecoval.translate("A_macrophytes_site_siteid",dict),
ecoval.translate("A_macrophytes_site_samplingdate",dict),
ecoval.translate("A_macrophytes_species_number_msk",dict),
ecoval.translate("A_macrophytes_species_name_latin",dict),
ecoval.translate("A_macrophytes_species_absolutecover_percent",dict),
ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_species_determinationuncertainty",dict),
ecoval.translate("A_macrophytes_species_phenology_blossom",dict),
ecoval.translate("A_macrophytes_species_phenology_fruits",dict))
ind.req <- match(req.colnames,colnames(data.species))
if ( anyNA(ind.req) )
{
species.quality <- rbind(species.quality,
c("","","",
paste(paste(ecoval.translate("R_macrophytes_error_speciescolmissing", dict),":",sep=""),
paste(req.colnames[is.na(ind.req)],collapse=", ")),
ecoval.translate("R_macrophytes_error_error", dict)))
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return list with generared outputs
return(list(species.assess = NA,
species.removed = NA,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# Eliminate rows without site and data entry:
data.species <- data.species[!is.na(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)]) & !is.na(data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),]
if ( nrow(data.species) == 0 )
{
species.quality <- rbind(species.quality,
c("","","",
ecoval.translate("R_macrophytes_error_nospeciesdata", dict),
ecoval.translate("R_macrophytes_error_error", dict)))
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return results
return(list(species.assess = NA,
species.removed = NA,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# Add unique identifier for individual assessments in extra column; and species within assessments as row names:
data.species <- data.frame(get.site.sampleid(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)],
data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]),
data.species, stringsAsFactors = FALSE)
colnames(data.species)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# Function to generate output for quality checks
split.last.underline <- function(x)
{
y <- strsplit(x,split="_")[[1]]
n <- length(y)
x2 <- y[n]
x1 <- NA
if ( n == 2 ) x1 <- y[1]
if ( n > 2 ) x1 <- paste(y[1:(n-1)],collapse="_")
return(c(x1,x2))
}
# Perform quality checks
# CHECK IF ALL CORE DATA REQUIRED AS UNIQUE IDENTIFIER ARE PRESENT
# # SITE CODE # *** note: lines without site code are now eliminated above
#
# ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_site_siteid",dict)]) # site code NA
# if ( sum(ind.na) > 0 ) {
# quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
# quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_site_siteid",dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
# colnames(quality.out) <- colnames(species.quality)
# species.quality <- rbind(species.quality, quality.out)
# }
#
# # DATE # *** note: lines without data are now eliminated above
#
# ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_site_samplingdate",dict)]) # sampling data NA
# if ( sum(ind.na) > 0 ) {
# quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
# quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_site_samplingdate",dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
# quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
# colnames(quality.out) <- colnames(species.quality)
# species.quality <- rbind(species.quality, quality.out)
# }
# SPECIES NUMBER
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)]) # species taxa list number NA
if( sum(ind.na) > 0 ) {
quality.out <- unique(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_number_msk",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# VALIDATE SPECIES DATA
# QUALITY CHECK OF OBLIGATORY PARAMETERS
# ABSOLUTE COVER OF INDIVIDUAL SPECIES
ind.taxalist <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)]
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
ind.bry <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
# Check if cover values are given for each species except bryophytes and characea
ind.illegal.na <- ind.na & !ind.bry & !ind.cha & ind.taxalist
if( sum( ind.illegal.na ) > 0 ) {
#quality.out <- unique(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_site_sampleid",dict)])
site_sample_species <- aggregate(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_species_number_msk",dict)],
by=list(data.species[ind.illegal.na,ecoval.translate("A_macrophytes_site_sampleid",dict)]),FUN=paste,collapse=",")
quality.out <- site_sample_species[,1]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, paste(rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)),":",site_sample_species[,2]))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if 0% cover values are present, which is either not correct or species should be not contained in data
ind.zero <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] == 0; ind.zero[is.na(ind.zero)] <- FALSE
if( sum(ind.zero) > 0 ) {
quality.out <- unique(data.species[ind.zero,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_value0percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if <0% cover values are present
ind.ltzero <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] < 0; ind.ltzero[is.na(ind.ltzero)] <- FALSE
if( sum(ind.ltzero) > 0 ) {
quality.out <- unique(data.species[ind.ltzero,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_valuelt0percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if >100% cover values are present
ind.gt100 <- data.species[,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] >= 100; ind.gt100[is.na(ind.gt100)] <- FALSE
if( sum(ind.gt100) > 0 ) {
quality.out <- unique(data.species[ind.gt100,ecoval.translate("A_macrophytes_site_sampleid",dict)])
quality.out <- cbind(t(sapply(quality.out, split.last.underline)), row.names = NULL)
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_valuegt100percent", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check consistency of sum bry and sum cha with brysum and chasum.
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("",length(Erhebung_Code))
for ( i in 1:length(dg.abs.check) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
if ( sum(ind.brysum&ind.sampling) > 0 & sum(ind.bry&ind.sampling) > 0 )
{
dg.brysum <- sum(data.species[ind.brysum&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.bry <- sum(data.species[ind.bry &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.bryall <- sum(data.species[ind.bry &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=FALSE)
if ( !is.na(dg.brysum) )
{
if ( !is.na(dg.bryall) ) # all species coverages available
{
if( abs(dg.brysum-dg.bryall) > 0.001 ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry",sep="")
}
else
{
if ( !is.na(dg.bry) ) # some species coverages available
{
if( dg.bry > dg.brysum ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry",sep="")
}
}
}
}
if ( sum(ind.chasum&ind.sampling) > 0 & sum(ind.cha&ind.sampling) > 0 )
{
dg.chasum <- sum(data.species[ind.chasum&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.cha <- sum(data.species[ind.cha &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE)
dg.chaall <- sum(data.species[ind.cha &ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=FALSE)
if ( !is.na(dg.chasum) )
{
if ( !is.na(dg.chaall) ) # all species coverages available
{
if( abs(dg.chasum-dg.chaall) > 0.001 ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha",sep="")
}
else
{
if ( !is.na(dg.cha) ) # some species coverages available
{
if( dg.cha > dg.chasum ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha",sep="")
}
}
}
}
}
if( sum(dg.abs.check == "bry", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[substring(dg.abs.check,1,3) == "bry"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumbryincompatible", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
if( sum(dg.abs.check == "cha",na.rm=T) > 0 | sum(dg.abs.check == "brycha", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "cha" | dg.abs.check == "brycha"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumchaincompatible", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check sum of cover values is > 100%.
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("ok",length(Erhebung_Code))
for ( i in 1:length(dg.abs.check) ) {
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
ind.test <- ind.sampling
if ( sum(ind.brysum&ind.sampling) > 0 ) ind.test <- ind.test & !ind.bry # if summary species present, exclude individual taxa
if ( sum(ind.chasum&ind.sampling) > 0 ) ind.test <- ind.test & !ind.cha # if summary species present, exclude individual taxa
if ( sum(data.species[ind.test,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)],na.rm=TRUE) > 100 )
{
dg.abs.check[i] <- "error"
}
}
if( sum(dg.abs.check == "error", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "error"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_sumabscoveragegt100%", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check if uncertainty of determination is given for each species, only if sampling protocol 2018 was used
if ( sampling.protocol == "v2018" ) {
ind.na <- is.na(data.species[,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)])
if( sum( ind.na ) > 0 ) {
site_sample_species <- aggregate(data.species[ind.na,ecoval.translate("A_macrophytes_species_number_msk",dict)],
by=list(data.species[ind.na,ecoval.translate("A_macrophytes_site_sampleid",dict)]),FUN=paste,collapse=",")
quality.out <- site_sample_species[,1]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_determinationuncertainty",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, paste(rep(ecoval.translate("R_macrophytes_error_completevalue", dict), nrow(quality.out)),":",site_sample_species[,2]))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
}
## Reduce data-set to species on taxalist
species.not.taxalist <- data.species[data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] == FALSE,]
data.species <- data.species[data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)],]
# Add column for comments regarding species validation (determination quality, see below)
data.species[, ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- NA
# Add growthform information to data
names <- colnames(data.species)
data.species <- cbind(data.species,NA,NA)
colnames(data.species) <- c(names,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict))
for( i in 1:nrow(data.species) ) {
row.taxalist <- match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
if( !is.na(row.taxalist) ) {
data.species[i, ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i, ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
}
}
# Check for double entries (only for taxa in taxa list).
Erhebung_Code <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
spec.num.check <- rep("ok",length(Erhebung_Code))
for ( i in 1:length(Erhebung_Code) ) {
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == Erhebung_Code[i]
spec.number <- data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)]
if ( length(spec.number) != length(unique(spec.number))) spec.num.check[i] <- "error"
}
if( sum(spec.num.check == "error", na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[spec.num.check == "error"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_number_msk",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_doubleentries", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Check species assignments and determination accuracy, and assign them to aggregates if this is adequate
# This is only done for data collected with sampling protocol published 2017/2018
# For old data species are taken as they are and determination uncertanity is not considered
rows.delete <- rep(FALSE,nrow(data.species))
if ( sampling.protocol == "v2018" ) {
for ( i in 1:nrow(data.species) ) {
row.taxalist <- match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[ ,ecoval.translate("A_macrophytes_species_number_msk",dict)])
detunc.data.species <- data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)]
if( !is.na(detunc.data.species) ) {
# Get corresponding determination value and taxa-group assigment from taxalist
detval.taxalist.dat <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_determination",dict)]
grouptaxon.taxalist.dat <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_taxagroups_assign",dict)]
# Do quality check for bryophyte species
if( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] == ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict) ) {
if ( ( detval.taxalist.dat <= 2 & detunc.data.species == 4 ) |
( detval.taxalist.dat == 3 & detunc.data.species != 2 ) ) {
if( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
} else {
rows.delete[i] <- TRUE
}
}
} else { # end if bryophyte
# Quality check for all higher macrophytes species
if ( detval.taxalist.dat <= 2 ) { # detval <= 2
if( detunc.data.species == 4 ) { # detunc = 4
if( !is.na(grouptaxon.taxalist.dat) ) { # aggregate tayon exists
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else { # no aggregate taxon
rows.delete[i] <- TRUE
}
} # end detunc = 4
} else { # detval > 2
if ( detval.taxalist.dat == 3 ) {
# For species with a determination value of 3 in the taxalist check if phenology trait necessary for correct
# determination of the taxon was present
phen.check.code <- taxalist.dat[match(data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[ ,ecoval.translate("A_macrophytes_species_number_msk",dict)]),
ecoval.translate("A_macrophytes_taxalist_phenology_trait",dict)]
phen.check.ok <- FALSE
# Code 1 for species in taxalist: Blossoms need to be present
if( phen.check.code == 1 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
} else {
# Code 2 for species in taxalist: Fruits need to be present
if( phen.check.code == 2 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)])
} else {
# Code 3 for species in taxalist: Blossoms OR fruits need to be present
if( phen.check.code == 3 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)]) | !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
} else {
# Code 3 for species in taxalist: Blossoms AND fruits need to be present
if( phen.check.code == 4 ) {
phen.check.ok <- !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_fruits",dict)]) & !is.na(data.species[i,ecoval.translate("A_macrophytes_species_phenology_blossom",dict)])
}
}
}
}
# Species is corrected to group-taxon if required phenology trait was not present or if determination was insecure
if ( !phen.check.ok | detunc.data.species > 3 ) {
if ( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else {
rows.delete[i] <- TRUE
}
}
} else {
if ( detval.taxalist.dat == 4 ) {
if ( !is.na(grouptaxon.taxalist.dat) ) {
data.species[i,ecoval.translate("A_macrophytes_species_number_msk",dict)] <- grouptaxon.taxalist.dat
data.species[i,ecoval.translate("A_macrophytes_species_name_latin",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
data.species[i,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- NA
data.species[i,ecoval.translate("A_macrophytes_species_commentvalidation_taxa",dict)] <- ecoval.translate("R_macrophytes_doc_speciesreplaced",dict)
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]
data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <- taxalist.dat[match(grouptaxon.taxalist.dat,taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk", dict)]),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]
} else {
rows.delete[i] <- TRUE
}
}
}
}
} # end not bryophyte
} # end if( !is.na(detunc.data.species) )
} # end for ( i in 1:nrow(data.species) )
} # end if ( sampling.protocol == "v2018" )
# Correct overall bryophyta and characea coverage:
if ( sum(rows.delete) > 0 )
{
for ( i in which(rows.delete) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] ==
data.species[i,ecoval.translate("A_macrophytes_site_sampleid",dict)]
# bryophytes:
if ( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict) )
{
if ( !is.na(data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) )
{
ind.brysum <- which(ind.sampling &
data.species[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict) )
if ( length(ind.brysum) == 1 )
{
data.species[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] =
data.species[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] -
data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
}
}
}
# characea:
if ( data.species[i,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict) )
{
if ( !is.na(data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) )
{
ind.chasum <- which(ind.sampling &
data.species[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict) )
if ( length(ind.chasum) == 1 )
{
data.species[ind.chasum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] =
data.species[ind.chasum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] -
data.species[i,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
}
}
}
}
}
# Remove species that did not conform to determination quality requirements
species.insuff.determ. <- data.species[rows.delete,]
data.species <- data.species[!rows.delete,]
# Aggregate douplicated species (can occur due to aggregation made above), aggregate algae,
# and add aggregate taxon for bryophytes and characea if needed:
ind.bry <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.species[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.delete <- rep(FALSE,nrow(data.species))
rows.add <- data.species[0,]
sampling.unique <- unique(data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)])
dg.abs.check <- rep("",length(sampling.unique))
for ( i in 1:length(sampling.unique) )
{
ind.sampling <- data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == sampling.unique[i]
ind.samp <- which(ind.sampling)
if ( length(ind.samp) > 0 )
{
# replace algae species by aggregate:
ind <- ind.samp[data.species[ind.sampling,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] ==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
if ( length(ind) > 0 )
{
for ( j in ind )
{
row.taxalist <- which(taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] ==
data.species[j,ecoval.translate("A_macrophytes_species_number_msk",dict)])
taxagroup.assign <- taxalist.dat[row.taxalist,ecoval.translate("A_macrophytes_taxalist_taxagroups_assign",dict)]
if ( !is.na(taxagroup.assign) )
{
data.species[j,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxagroup.assign
data.species[j,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[which(taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)] == taxagroup.assign),
ecoval.translate("A_macrophytes_species_name_latin",dict)]
}
}
}
# add aggregate taxa for bryophytes and characea if needed:
if ( sum(ind.bry&ind.sampling) > 0 & sum(ind.brysum&ind.sampling)==0 )
{
rows.add <- rbind(rows.add,rep(NA,ncol(rows.add)))
colnames(rows.add) <- colnames(data.species)
ind.add <- nrow(rows.add)
ind.copy <- which(ind.bry&ind.sampling)[1]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_siteid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_siteid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_samplingdate",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_samplingdate",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_sampleid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_sampleid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_name_latin",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)] <-
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]*
data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)])/
sum(data.species[ind.bry&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- 1
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_assess_bryophyte",dict)
if ( is.na(rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) ) dg.abs.check[i] <- paste(dg.abs.check[i],"bry")
}
if ( sum(ind.cha&ind.sampling) > 0 & sum(ind.chasum&ind.sampling)==0 )
{
rows.add <- rbind(rows.add,rep(NA,ncol(rows.add)))
colnames(rows.add) <- colnames(data.species)
ind.add <- nrow(rows.add)
ind.copy <- which(ind.cha&ind.sampling)[1]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_siteid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_siteid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_samplingdate",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_samplingdate",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_site_sampleid",dict)] <-
data.species[ind.copy,ecoval.translate("A_macrophytes_site_sampleid",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_number_msk",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_name_latin",dict)] <-
taxalist.dat[,ecoval.translate("A_macrophytes_species_name_latin",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)] <-
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]*
data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict)])/
sum(data.species[ind.cha&ind.sampling,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.add[ind.add,ecoval.translate("A_macrophytes_species_determinationuncertainty",dict)] <- 1
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)
rows.add[ind.add,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] <-
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)
if ( is.na(rows.add[ind.add,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) ) dg.abs.check[i] <- paste(dg.abs.check[i],"cha")
}
# aggregate identical taxa:
species_no <- unique(data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)])
for ( j in 1:length(species_no) )
{
ind <- ind.samp[data.species[ind.sampling,ecoval.translate("A_macrophytes_species_number_msk",dict)] == species_no[j]]
if ( length(ind) > 1 )
{
data.species[ind[1],ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
sum(data.species[ind,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
rows.delete[ind[2:length(ind)]] <- TRUE
}
}
}
}
data.species <- data.species[!rows.delete,]
if ( nrow(rows.add) > 0 ) data.species <- rbind(data.species,rows.add)
if( sum(dg.abs.check == "bry",na.rm=T) > 0 ) {
quality.out <- Erhebung_Code[substring(dg.abs.check,1,3) == "bry"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_abscoveragebrymissing", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
if( sum(dg.abs.check == "cha",na.rm=T)>0 | sum(dg.abs.check == "brycha",na.rm =T) > 0 ) {
quality.out <- Erhebung_Code[dg.abs.check == "cha" | dg.abs.check == "brycha"]
quality.out <- cbind(t(sapply(quality.out , split.last.underline)))
quality.out <- cbind(quality.out, rep(ecoval.translate("A_macrophytes_species_absolutecover_percent",dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_abscoveragechamissing", dict), nrow(quality.out)))
quality.out <- cbind(quality.out, rep(ecoval.translate("R_macrophytes_error_error", dict), nrow(quality.out)))
colnames(quality.out) <- colnames(species.quality)
species.quality <- rbind(species.quality, quality.out)
}
# Combine all data containing removed species, either due to taxalist or insufficient determination, in one data.frame
species.insuff.determ.[,ecoval.translate("A_macrophytes_species_reason_elimination",dict)] <- rep(ecoval.translate("A_macrophytes_species_reason_insufficientquality",dict), nrow(species.insuff.determ.))
species.not.taxalist[,ecoval.translate("A_macrophytes_species_reason_elimination",dict)] <- rep(ecoval.translate("A_macrophytes_species_reason_notontaxalist",dict), nrow(species.not.taxalist))
# Remove additional columns in species.insuff.determ. introduced during data perparation after reductioh to species not on taxalist
species.insuff.determ. <- species.insuff.determ.[,colnames(species.insuff.determ.) %in% colnames(species.not.taxalist)]
# Create output data.frame with all removed species
species.removed <- rbind(species.insuff.determ., species.not.taxalist, stringsAsFactors = FALSE)
# Complement "species.quality" data.frame with data-set information
species.quality <- cbind(species.quality, rep(ecoval.translate("R_macrophytes_error_speciesdata", dict), nrow(species.quality)))
colnames(species.quality)[ncol(species.quality)] <- ecoval.translate("R_macrophytes_error_dataset", dict)
# Return list with generated outputs
return(list(species.assess = data.species,
species.removed = species.removed,
species.quality = species.quality,
taxalist = taxalist.dat))
}
# ================================
# FUNCTION TO CALCULATE ATTRIBUTES
# ================================
msk.macrophytes.2017.calc.attrib <- function(data.site,
data.species,
language = "English",
dictionaries = NA)
{
# get dictionary:
dict <- ecoval.dict(language,dictionaries)
# get taxa list:
taxalist.dat <- ecoval::msk.macrophytes.2017_ListTaxa
for ( j in 1:ncol(taxalist.dat) ) colnames(taxalist.dat)[j] <- ecoval.translate(colnames(taxalist.dat)[j],dict)
col.gf <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict),colnames(taxalist.dat))
if ( !is.na(col.gf) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gf] <- ecoval.translate(taxalist.dat[i,col.gf],dict)
col.gfabbrev <- match(ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),colnames(taxalist.dat))
if ( !is.na(col.gfabbrev) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.gfabbrev] <- ecoval.translate(taxalist.dat[i,col.gfabbrev],dict)
col.es <- match(ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfo",dict),colnames(taxalist.dat))
if ( !is.na(col.es) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.es] <- ifelse(is.na(taxalist.dat[i,col.es]),NA,ecoval.translate(taxalist.dat[i,col.es],dict))
col.trait <- match(ecoval.translate("A_macrophytes_taxalist_determination_trait",dict),colnames(taxalist.dat))
if ( !is.na(col.trait) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.trait] <- ifelse(is.na(taxalist.dat[i,col.trait]),NA,ecoval.translate(taxalist.dat[i,col.trait],dict))
col.cons <- match(ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict),colnames(taxalist.dat))
if ( !is.na(col.cons) ) for ( i in 1:nrow(taxalist.dat) ) taxalist.dat[i,col.cons] <- ifelse(is.na(taxalist.dat[i,col.cons]),NA,ecoval.translate(taxalist.dat[i,col.cons],dict))
# Create data.frame to collect calculated attributes
attrib.names <- c(ecoval.translate("A_macrophytes_taxa_all_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_all_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict),
ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict),
ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict),
ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict),
ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict),
ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict),
ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict),
ecoval.translate("A_macrophytes_priorityspecies1_count",dict),
ecoval.translate("A_macrophytes_priorityspecies2_count",dict),
ecoval.translate("A_macrophytes_priorityspecies3_count",dict),
ecoval.translate("A_macrophytes_priorityspecies4_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic",dict),
ecoval.translate("A_macrophytes_taxa_helophytes",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes",dict))
attrib.dat <- data.frame(matrix(NA, nrow = nrow(data.site), ncol = length(attrib.names)), stringsAsFactors = FALSE)
colnames(attrib.dat) <- attrib.names
rownames(attrib.dat) <- rownames(data.site)
# Fill data.frame with entries as if no vegetation is present at sites. For sites with vegetation cover these values will be
# later replaced by calculated attribute values
attrib.dat[, c(ecoval.translate("A_macrophytes_taxa_all_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_all_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict),
ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict),
ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict),
ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict),
ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict),
ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict))] <- 0
# Calculate attributes for all data contained in data.site
for ( i in 1:nrow(attrib.dat) ) {
# Create data.frame with vegetation data of sampling i:
data.select <- data.species[data.species[,ecoval.translate("A_macrophytes_site_sampleid",dict)] == rownames(attrib.dat)[i],
c(ecoval.translate("A_macrophytes_species_name_latin",dict),
ecoval.translate("A_macrophytes_species_number_msk",dict),
ecoval.translate("A_macrophytes_species_absolutecover_percent", dict),
ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent",dict),
ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict),
ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict))]
# get indices of bryophytes and characea:
ind.bry <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.brysum <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.cha <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.chasum <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
ind.alg <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
ind.helo <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_helophyte",dict)]
ind.aquatic <- data.select[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)]
if( nrow(data.select) > 0 ) {
## ABSOLUTE COVER / BIOMASS ATTRIBUTES
# Absolute cover of all vegetation at site
attrib.dat[i, ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict)] <-
sum(data.select[!(ind.bry|ind.cha),ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
# Calculate and add relative cover per species (Colname: "Rel_Deckung") in selected vegetation data
data.select$Rel_Deckung <- data.select[, ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] * 100 /
attrib.dat[i, ecoval.translate("A_macrophytes_allvegetation_abscover_percent",dict)]
# Absolute cover of only higher macrophytes
attrib.dat[i, ecoval.translate("A_macrophytes_allmacrophytes_abscover_percent",dict)] <-
sum(data.select[!(ind.bry|ind.cha|ind.alg),ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
# Add relevant information from taxalist to data-set
data.select$neophyte_info <- rep(NA, nrow(data.select))
data.select$priority <- rep(NA, nrow(data.select))
data.select$guide_value <- rep(NA, nrow(data.select))
for (j in 1:nrow(data.select)) {
row.taxalist <- match(data.select[j,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
data.select[j, "neophyte_info"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_conservationinfo",dict)]
data.select[j, "priority"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_priorityspecies",dict)]
data.select[j, "guide_value"] <- taxalist.dat[row.taxalist, ecoval.translate("A_macrophytes_taxalist_indexspecies",dict)]
}
# Generate data.frame for calculation of richness and dominance by bundling species that can be emerged or submerged
data.select.richness <- data.select
rows.delete <- rep(FALSE,nrow(data.select.richness))
for ( j in 1:nrow(data.select.richness) )
{
row.taxalist <- match(data.select.richness[j,ecoval.translate("A_macrophytes_species_number_msk",dict)],
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
number.msk.grfoval <- taxalist.dat[row.taxalist,ecoval.translate("A_macrophytes_taxalist_growthform_assess_twoforms_grfoval",dict)]
if ( !is.na(number.msk.grfoval) )
{
row.data.select <- match(number.msk.grfoval,data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)])
if ( !is.na(row.data.select) )
{
# add absolute coverage from row that should be eliminated:
data.select.richness[row.data.select,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] <-
data.select.richness[row.data.select,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)] +
data.select.richness[j,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]
# add absolute coverage from row that should be eliminated:
data.select.richness[row.data.select,"Rel_Deckung"] <-
data.select.richness[row.data.select,"Rel_Deckung"] +
data.select.richness[j,"Rel_Deckung"]
# mark row for deletion:
rows.delete[j] <- TRUE
}
}
}
if ( sum(rows.delete) > 0 ) data.select.richness <- data.select.richness[!rows.delete,]
# get indices of bryophytes and characea:
ind.richness.bry <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_bry",dict)]
ind.richness.brysum <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_brysum",dict)]
ind.richness.cha <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_cha",dict)]
ind.richness.chasum <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_abbrev_chasum",dict)]
ind.richness.alg <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict)]
ind.richness.helo <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_helophyte",dict)]
ind.richness.aquatic <- data.select.richness[,ecoval.translate("A_macrophytes_species_number_msk",dict)] %in%
taxalist.dat[,ecoval.translate("A_macrophytes_species_number_msk",dict)][taxalist.dat[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)]==
ecoval.translate("L_macrophytes_taxalist_growthform_assess_aquatic",dict)]
# # Generate data.frames with only data on higher macrophytes (i.e. no algae)
# dat.makro <- data.select[data.select[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] != ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict),]
# dat.makro.richness <- data.select.richness[data.select.richness[,ecoval.translate("A_macrophytes_taxalist_growthform_assess",dict)] != ecoval.translate("L_macrophytes_taxalist_growthform_assess_algae",dict),]
## DIVERSITY ATTRIBUTES
# SPECIES RICHNESS
# number of summary taxa and algae:
num.bry <- sum(ind.richness.bry); if( num.bry==0 & sum(ind.richness.brysum)>0 ) num.bry <- 1
num.aquatic <- sum(ind.richness.aquatic) - sum(ind.richness.chasum); if ( sum(ind.richness.cha)==0 & sum(ind.richness.chasum)>0 ) num.aquatic <- num.aquatic+1
num.helo <- sum(ind.richness.helo)
num.alg <- sum(ind.richness.alg)
# Species richness of all growthforms, without algae:
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_all_richness_count",dict)] <- num.bry + num.aquatic + num.helo
# Species richness per growthform group (i.e. aquatic, helophyte, bryophyte)
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_richness_count",dict)] <- num.aquatic
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_richness_count",dict)] <- num.helo
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_richness_count",dict)] <- num.bry
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_neophytes_richness_count",dict)] <-
sum(data.select.richness[!(ind.richness.brysum|ind.richness.chasum|ind.richness.alg),"neophyte_info"] == "N",na.rm=T)
# GROWTHFORM RICHNESS
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_all_richness_count",dict)] <-
length(na.omit(unique(data.select[!(ind.brysum|ind.chasum|ind.alg),ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_helophytes_richness_count",dict)] <-
length(na.omit(unique(data.select[ind.helo,ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
attrib.dat[i,ecoval.translate("A_macrophytes_growthform_aquatic_richness_count",dict)] <-
length(na.omit(unique(data.select[ind.aquatic&(!ind.chasum),ecoval.translate("A_macrophytes_taxalist_growthform_abbrev",dict)])))
## COMMUNITY COMPOSITION
# HEIPs DOMINANCE INDEX (calculated with data.select.richness)
#dat.rel.sw <- data.select.richness[!(ind.richness.bry|ind.richness.cha|ind.richness.alg), "Rel_Deckung"]
dat.rel.sw <- data.select.richness[!(ind.richness.bry|ind.richness.cha), "Rel_Deckung"] # previous implementation was with algae
n.taxa.sw <- length(na.omit(dat.rel.sw))
sw.index.bry <- -(sum((dat.rel.sw/100) * log((dat.rel.sw/100))))
if ( n.taxa.sw == 1 ) sw.index.bry <- 0
if ( n.taxa.sw == 0 ) sw.index.bry <- NA
attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- (exp(sw.index.bry) - 1) / (n.taxa.sw - 1) #ATTRIBUT
if ( n.taxa.sw == 1 ) attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- 0 # Minimaler Wert fuer Heip Index ist 0.006 (cf. Maggurran A., Measuring biological diversity )
if ( n.taxa.sw == 0 ) attrib.dat[i, ecoval.translate("A_macrophytes_heipindex_bryophyteonespecies",dict)] <- NA
rm(list = c("dat.rel.sw", "n.taxa.sw", "sw.index.bry"))
# NEOPHYTES RELATIVE COVER
ind.neo <- data.select[, "neophyte_info"] == "N"; ind.neo <- ifelse(is.na(ind.neo),FALSE,ind.neo)
attrib.dat[i,ecoval.translate("A_macrophytes_neophytes_relcover_percent",dict)] <-
sum(data.select[ind.neo , "Rel_Deckung"]) #ATTRIBUT
# RELATIVE COVER OF GROWTHFORMS (i.e. AQUATIC, HELOPHYTES, and BRYOPHYTES)
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] <-
sum(data.select[ind.aquatic&!ind.cha,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] <-
sum(data.select[ind.helo,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] <-
sum(data.select[ind.brysum,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] <-
sum(data.select[ind.alg,"Rel_Deckung"])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_relcover_percent",dict)] <- 100
}
# ADJUST RELATIVE COVER BRYOPHYTES TO PROPORTION ON ARTIFICIAL SUBSTRATE FOR ASSESSMENT
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <- NA
if ( !is.na(attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)]) )
{
if( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] > 0 ) {
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)] <-
data.select[ind.brysum,ecoval.translate("A_macrophytes_species_artificialsubstrate_relcover_percent", dict)] # Attribute for illustrative purposes in value function
if( is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]) ) {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <-
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] #ATTRIBUT
} else {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <-
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] *
(1 - attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]/100) +
0.33 * attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_relcover_percent",dict)] *
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_artificialsubstrate_relcover_percent",dict)]/100 #ATTRIBUT
}
} else {
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_adjusted_relcover_percent",dict)] <- 0
}
}
# ABSOLUTE COVER BY GROWTHFORMS, i.e. AQUATIC, HELOPHYTES, and BRYOPHYTES
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] <-
sum(data.select[ind.aquatic&!ind.cha,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] <-
sum(data.select[ind.helo,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)]) )
{
if ( attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] <-
sum(data.select[ind.brysum,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)])
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes_abscover_percent",dict)] <- 100
}
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] <-
sum(data.select[ind.alg,ecoval.translate("A_macrophytes_species_absolutecover_percent",dict)]) #ATTRIBUT
if ( !is.na(attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)]) )
{
if ( attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] - 100 > 0 &
attrib.dat[i, ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] - 100 < 0.01 )
attrib.dat[i,ecoval.translate("A_macrophytes_filamentousgreenalgae_abscover_percent",dict)] <- 100
}
## SPECIES QUALITY / CONSERVAT
# PRIORITY SPECIES
# NUMBER OF SPECIES FOR EACH PRIORITY CATEGORY
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 1, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies1_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 2, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies2_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 3, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies3_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)] <- sum(data.select[!ind.alg,"priority"] == 4, na.rm = T) #ATTRIBUT
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_priorityspecies4_count",dict)] <- 0
}
# GUIDE VALUES, NUMBER OF SPECIES ("Leitarten")
# Anzahl Arten mit spezifischem Leitwert
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 1, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac1_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 2, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac2_count",dict)] <- 0
}
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)] <-
sum(data.select[!(ind.alg|ind.bry|ind.brysum),"guide_value"] == 3, na.rm = T)
if( abs(attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)]) == Inf ) {
attrib.dat[i,ecoval.translate("A_macrophytes_indexspeciesmac3_count",dict)] <- 0
}
# BRYOPHYTES
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 1, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry1_count",dict)] <- 0
}
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 2, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry2_count",dict)] <- 0
}
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)] <- sum(data.select[ind.bry,"guide_value"] == 3, na.rm = T)
if( abs(attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)]) == Inf ) {
attrib.dat[i, ecoval.translate("A_macrophytes_indexspeciesbry3_count",dict)] <- 0
}
## Collect taxon names grouped by growthform ZH
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_aquatic",dict)] <-
paste(data.select[ind.aquatic&!ind.chasum,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_helophytes",dict)] <-
paste(data.select[ind.helo,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
attrib.dat[i,ecoval.translate("A_macrophytes_taxa_bryophytes",dict)] <-
paste(data.select[ind.bry,ecoval.translate("A_macrophytes_species_name_latin",dict)], collapse = "; ")
rm(list = c("data.select"))
}
}
# Return results
# Data tables as tab-delimited text-files to output directiory
# For this output the sampling event code is written in the first column
# attrib.out <- data.frame(rownames(attrib.dat), attrib.dat, stringsAsFactors = FALSE)
# colnames(attrib.out)[1] <- ecoval.translate("A_macrophytes_site_sampleid",dict)
# write.table(attrib.out, paste(OutputDir,"AttributeData_Final.dat", sep = ""), quote = FALSE, row.names = FALSE, na = "", sep = "\t")
# Generated data.frame to environment
return(attrib.dat)
}
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