R/data wrangling/ellison_2018.R

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

## ellison_2018
library(data.table)

dataset_id <- 'ellison_2018'
load(file = 'data/raw data/ellison_2018/ddata')
setDT(ddata)

setnames(ddata,
         old = c('block','plot','subplot','subplot.t', 'count'),
         new = c('site','block','plot','subplot', 'value'))

ddata[, ':='( year = as.integer(format(date, '%Y')),
              species = paste(genus, species))]
ddata <- ddata[!is.na(value) & value > 0 &
                  !(date > '2006-08-31' & date < '2006-12-31') &
                  !(site == 'Ridge' & block == 4 & year == 2006) & # Only one block
                  !(site == 'Valley' & block == 3 & year == 2006)] # Only one block



# Community
# pooling subplots among plots (each plot has 3 subplots that are treatments, not replicates so subplots aka treatments of several plots are pooled together) and subplot is then renamed plot for consistency.
ddata[, n_trap := length(unique(plot)), by = .(site, block, subplot, treatment, year, date)]
ddata <- ddata[, .(value = sum(value)), by = .(site, block, subplot, treatment, year, date, n_trap, species)]
setnames(ddata, 'subplot', 'plot')
ddata[, ':='(
   N = sum(value),
   S = length(unique(species)),
   ENSPIE = vegan::diversity(x = value, index = 'invsimpson')
),
by = .(site, block, plot, year, date)
]

ddata[, minN := min(N), by = .(site, block, plot)]

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

ddata[, ':='(
   singletons = sum(value == 1),
   doubletons = sum(value == 2)
), by = .(site, block, plot, year, date)
][,
  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)) * n_trap, by = .(site, year, block, plot)]

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


ddata[, ':='(dataset_id = dataset_id,
             treatment = paste(
                gsub(' control', '_management', treatment),
                plot, sep = '_')
)][, ':='(
   treatment_type = "manipulated community",
   grain_m2 = pi*0.0475^2 * n_trap,
   grain_comment = "'Each pitfall trap consisted of a 95mm-diameter plastic cup'",
   design = paste0('A', fifelse(grepl('control', treatment), 'C', 'I')),
   timepoints = paste0('T',seq_along(unique(year))[match(year, sort(unique(year)))]),
   time_since_disturbance = fifelse(grepl('control', treatment), NA_integer_, year - 2006),
   realm = 'terrestrial',
   taxon = 'invertebrates',

   comment = 'Hierarchical experimental design. Treatment is one of 8 canopy manipulation treatments. Effort is the number of surveys per year * the number of pitfall traps, each survey corresponds to 1 or 2 pitfall traps being open/active for 48 hours (Ellison 2005).',

   n_trap = NULL
)
]



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