R/data wrangling/joern_2020.r

In chase-lab/BioTIMEx: Aggregating long-term ecological communities data

## joern_2020
library(data.table)


dataset_id <- 'joern_2020'
load(file = 'data/raw data/joern_2020/ddata')
setDT(ddata)

setnames(ddata, old = c('Recyear','Watershed','Repsite'),
         new = c('year','site','block'))


ddata[, ':='(
   site = toupper(site),
   block = toupper(block),
   species = paste(Genus, Species),
   date = as.Date(paste(year, Recmonth, Recday, sep = '-'))
)
][site %in% c("0C1A", "0C3A", "0C3B", "0C3C"),
  site := c('C01A','C03A','C03B','C03C')[match(site, c("0C1A", "0C3A", "0C3B", "0C3C"))]
]

# Excluding surveys happening in August or September, exclusion of July samplings
# exclusion of 2012 survey for one block bcause N = 5
ddata <- ddata[Recmonth > 7 &
                  !(site == 'C03A' & year == 2012 & block == 'C')]


ddata[, treatment := paste(
   fifelse(substr(site, 1, 1) %in% c('n','N'), 'Grazing', 'noGrazing'),
   fifelse(substr(site, 2, 3) == '00',
           'noFire',
           paste0('fire', substr(site, 2, 3) )), sep = '_')
][, treatment := fifelse(treatment == 'noGrazing_noFire', 'control', treatment)]


# melting sites and selecting columns
ddata <- melt(ddata,
              id.vars = c('site', 'block', 'treatment', 'year','date','species'),
              measure.vars = paste0('S', 1:10),
              variable.name = 'plot',
              value.name = 'value'
)

ddata <- ddata[!is.na(value) & value > 0]
# ddata <- ddata[, .(value = sum(value)), by = .(site, block, treatment, year, species)] # Pooling by block is the same as taking the total column from raw data






# Community
ddata[, ':='(
   N = sum(value),
   S = length(unique(species)),
   ENSPIE = vegan::diversity(x = value, index = 'invsimpson')
),
by = .(site, block, treatment, date)
]

ddata[, minN := min(N), by = .(site, block, treatment)] # 100% > 5

ddata[, Sn := vegan::rarefy(value, sample = minN), by = .(site, block, treatment, date)]

ddata[, ':='(
   singletons = sum(value == 1),
   doubletons = sum(value == 2)
), by = .(site, block, treatment, year)
][,
  coverage := fifelse(
     doubletons > 0,
     1 - (singletons/N) * (((N - 1)*singletons)/((N - 1)*singletons + 2*doubletons)),
     1 - (singletons/N) * (((N - 1)*singletons)/((N - 1)*singletons + 2))
  )][, ':='(singletons = NULL, doubletons = NULL)]

ddata[, effort := length(unique(date)), by = .(site, block, treatment, year)]


ddata <- ddata[,
               lapply(.SD, mean),
               by = .(site, block, treatment, year),
               .SDcols = c('effort','N','minN','S','Sn','ENSPIE')
]




ddata[, ':='(
   dataset_id = dataset_id,
   treatment_type = "fire and grazing",
   grain_m2 =  pi*0.38^2,
   grain_comment = "'Sampling is done by sweeping with canvas beating nets 38 cm in diameter. A sample of 200 sweeps (ten sets of 20 sweeps each) is taken at each site on each occasion. A sweep is taken at each step by traversing an arc of 180o with the net through the top layer of vegetation.'",
   design = paste0('A', fifelse(treatment == 'control', 'C', 'I')),
   timepoints = paste0('T', seq_along(unique(year))[match(year, sort(unique(year)))])
)]

# Disturbance calendar
load('data/raw data/joern_2019/ddata_env')
fd <- fd[nchar(fd$Watershed) <= 4,]

# N4D -> N04D
pattern <- "(^[A-Za-z]){1}([0-9]){1}([A-Za-z]){1}$"
replacement <- "\\10\\2\\3"
fd$Watershed <- gsub(pattern, replacement, fd$Watershed)
# 1B -> 001B
fd$Watershed <- toupper( paste0(
   sapply(max(nchar(fd$Watershed)) - nchar(fd$Watershed), function(x) paste(rep(x = 0, times = x), collapse = "")),
   fd$Watershed
) )

fd <- fd[fd$Watershed %in% ddata$site, ]




time_since_disturbance <- rep(NA, nrow(ddata))
suppressWarnings({
   for (i in 1:nrow(ddata))  {
      last_fire_date <- max(fd[fd$Watershed == ddata$site[i] & fd$Year <= ddata$year[i], 'Year'])
      time_since_disturbance[i] <- as.numeric( ddata$year[i] - last_fire_date )
   }
})
time_since_disturbance[is.infinite(time_since_disturbance)] <- NA

ddata[, ':='(
   time_since_disturbance = time_since_disturbance,
   realm = 'terrestrial',
   taxon = 'invertebrates',

   comment = 'Grasshopper sampling in fields burnt at various frequencies (the number in the site names indicates the theoretical frequency). Some fields are also grazed and the site name indicates N for natural grazing by bison. Most of the type, there were several samplings in a year, they have been pooled together and divided by the number of visits (1 to 14).'
)]


# Total abundance is different from the sum of the abundances in S1:S10 columns in a significant number of cases


dir.create(paste0('data/wrangled data/', dataset_id), showWarnings = FALSE)
fwrite(ddata, paste0('data/wrangled data/', dataset_id, "/", dataset_id, '.csv'), row.names = FALSE)