Insect Data

# read in the insect survey data: this data set has all individuals, including
  ## dead trees. The data areh also displayed as raw counts, i.e. not cpue.
  ## This is based off of the conversations with the statistical consulting
  ## advisor in 2018. - The data are most indicative of a zero-inflated poisson
  ## distribution.
insect.surveys <- read.csv(file = "data/insect-surveys.csv",header = TRUE,
                           strip.white = TRUE, na.strings = c("","NA"))

# set columns to appropriate type
insect.surveys <- within(insect.surveys, {
  # Change to from numeric to factor
  Block = factor(Block) # block identifier
  Position = factor(Position) # column in plot
  SerialNo = factor(SerialNo) # unique tree identifier
  Survey.Year = factor(Survey.Year) # Year of ovservation
  # reorder levels appropriately
  survey.event = factor(survey.event, # survey event ID
                         levels = c("jun16","aug16","jun17","aug17"))
  Survey.Month = factor(Survey.Month, # calendar month of observation
                         levels = c("Jun","Jul","Aug"))
  # add correct date format
  Date = as.Date(Date)
  # change from factor to character
  Notes = as.character(Notes)
  # remove empty columns - initially unkown galls, later removed
  Pelican.Beak.Gall = NULL
  Open.Brown.Leaf.Gall = NULL
})

# remove trees that were not part of the surveys (border trees)
insect.surveys <- insect.surveys %>% filter(!is.na(Min.per.Tree))

insect meta-data

# read in column meta data (already as factors)
metadata.insects <- read.csv("data/insect-col-metadata.csv", na.strings = c("","NA","<NA>"),
                             stringsAsFactors = TRUE,strip.white = TRUE)

Weather Data

# 2016
weather.2016 <- read.csv(file = "data/weather/2016_weather.csv", 
                         na.strings = "", strip.white = TRUE)
# 2017
weather.2017 <- read.csv(file = "data/weather/2017_weather.csv", 
                         na.strings = "", strip.white = TRUE)
# check that the columns are the same
stopifnot(all(names(weather.2016) == names(weather.2017)))
# combine them into 1 data set
weather.data <- rbind(weather.2016,weather.2017)

weather.data <- within(weather.data,{
  # change the "T" in precip. to NA
  avg.precip_in = ifelse(weather.data$avg.precip_in == "T",
                         yes =  NA,
                         no = as.numeric(as.character(
                           weather.data$avg.precip_in)))
  # formate the date column
  date = as.Date(dmy(date))
  # break the 'events' column into 3 flag columns: fog, rain, thunder
  fog.event = as.factor(ifelse(is.na(events),yes = NA,
               no = ifelse(grepl(events,pattern = "Fog"),yes = 1,no = 0)
               ))
  rain.event = as.factor(ifelse(is.na(events),yes = NA,
               no = ifelse(grepl(events,pattern = "Rain"),yes = 1,no = 0)
               ))
  thunderstorm.event = as.factor(ifelse(is.na(events),yes = NA,
               no = ifelse(grepl(events,pattern = "Thunderstorm"),
                           yes = 1,no = 0)
               ))
  events = NULL
})
# remove single year data objects
# rm(list = c("weather.2016","weather.2017"))

Phenotype Data

# # Read in the phenotype data table. This file is created and maintained by
#   ## Chris Cole. This file should be kept updated in the directory. 
# trait.data <- read.csv(file = "data/trait-data.csv",strip.white = TRUE,
#                        na.strings = c("","NA"))

# # select only relevent columns
#   ## important. Then comment columns will be removed
# keep.cols <- grep(names(trait.data), value = TRUE,
#      pattern = "SerialNo|.*1(6|7|8)|PlantingDate|Sex.Genet|
#      ^ExclusionFlag$|Latitude|Longitude|Dist.Edge|Ploidy",
#      perl = TRUE) %>% 
#   ## remove comment columns
#   grep(pattern = "Comment",invert = TRUE, value = TRUE)
# # reassign the data
# trait.data <- trait.data %>% select(keep.cols)
# 
# # fix column types
# trait.data <- within(trait.data,{
#   SerialNo = factor(SerialNo, levels = levels(insect.surveys$SerialNo))
#   Ploidy = factor(Ploidy)
#   BBreakDegDay.2016 = as.numeric(as.character(BBreakDegDay.2016))
# })
# 
# # remove 'keep.cols' object
# # rm(list = "keep.cols")

trait.data <- read.csv(file = "data/trait-data-long.csv", header = TRUE, strip.white = TRUE, na.strings = c("","NA"))
trait.data <- within(trait.data,{
  SerialNo <- factor(SerialNo)
  Block <- factor(Block)
  year.proj <- factor(year.proj)
})
trait.data <- trait.data %>% filter(year %in% c(2016,2017), month != "avg") %>% select(-c(Tree.ID, Block, Row, Position))

All Insects

all.insects <- all.insect.cols # copy and rename all.insect.cols

# Add summary of all insects to diversity table
## complete counts
grouped.insects.data[,"all.insects_cnt"] <- # This column is analagous to richness
  rowSums(insect.surveys[,names(insect.surveys) %in% all.insects],
          na.rm = TRUE)
## presence data
grouped.insects.data[,"all.insects_sp.cnt"] <-
rowSums(insect.surveys[,names(insect.surveys)%in%all.insects]>0,
        na.rm = TRUE)

Here are summary statistics of the insect counts and spp. counts for all insects:

grouped.insects.data %>% 
               select(all.insects_cnt, all.insects_sp.cnt) %>% 
               summary() %>% knitr::kable()

Aphids skew the maximum total number of insects counted. Interestingly, no tree had all common insect species present during any survey event.

Functional Groups

There are various functional groups at WisAsp as well (Ant, Free.Feeding, Leaf.Modifying, Wood.Modifying) which group insects according to how they feed on the trees.

# add up all insects according to functional group
func.grps <- list() # create empty list to fill
for (FG in levels(metadata.insects$Functional.Group)) {
  # create sublist for each functional group and fill it with insects
    ## from that group 
  func.grps[[FG]] <- (metadata.insects %>% 
                        # filter according to functional group and col classif.
                        ## 'insect species' ensures only species, not groups 
                          ##like 'sawflies' or 'leafhoppers' are present:
                        filter(Functional.Group == FG,
                               Classification == "insect species") %>%
                        select(R.Columns))[,1] #`[,1]` removes rownames

  # Add summary of all FGs to diversity table    
  ## complete counts
  grouped.insects.data[,paste0(FG,"_cnt")] <-
    rowSums(insect.surveys[,names(insect.surveys)%in%func.grps[[FG]]],
            na.rm = TRUE)
  ## presence data
  grouped.insects.data[,paste0(FG,"_sp.cnt")] <-
  rowSums(insect.surveys[,names(insect.surveys)%in%func.grps[[FG]]]>0,
          na.rm = TRUE)
}
# rm(FG) # remove iterator
grouped.insects.data$Ant_sp.cnt <- NULL # remove ant species count column

Internal or External Feeders

Finally, the insects can be grouped according to their feeding location. Insects that feed within the wood or leaves are endophages and those that feed externally are ectophages.

# add up all insects according to feeding location
feed.loc <- list() # empty list
for (LOC in levels(metadata.insects$Feeding.Location)) {
  # same as w/`func.grps` except with feeding locations
  feed.loc[[LOC]] <- (metadata.insects %>% 
                        filter(Feeding.Location == LOC, # see prev. chunk
                               Classification == "insect species") %>%
                      select(R.Columns))[,1] #`[,1]` removes rownames
  
  # Add summary of all feeding locations to diversity table    
  ## complete counts
  grouped.insects.data[,paste0(LOC,"_cnt")] <-
    rowSums(insect.surveys[,names(insect.surveys)%in%feed.loc[[LOC]]],
            na.rm = TRUE)
  ## presence data
  grouped.insects.data[,paste0(LOC,"_sp.cnt")] <-
  rowSums(insect.surveys[,names(insect.surveys)%in%feed.loc[[LOC]]]>0,
          na.rm = TRUE)
}
# rm(LOC) # remove iterator