carabids_03_EDA_carabid.R

# This script explores the differences in species ED between the parataxonomist, expert taxonomist, and barcode datasets on NEON ground beetles, then visualizes the data
# Para vs expert comparison written by Max

library(dplyr)
library(stringr) #word()
library(ggplot2)
library(forcats) #fct_reorder
library(arm) #display

# Load carabid data
load(file="data_derived/carabids_NIWO.Rdata")
list2env(carabid_abund, .GlobalEnv)  
list2env(carabid_barcode, .GlobalEnv)  


### First compare barcode and expert taxonomist

barcode <- bet_BOLDtaxonomy %>%
            mutate(individualID = sampleID, bc_sciname = NA) %>%
            as_tibble

expert <- bet_expertTaxonomistIDProcessed %>%
            mutate(expert_sciname = paste(genus, specificEpithet)) %>%
            as_tibble

# Some barcode species are subspecies. Remove subspecies, leaving genus/species
for (i in 1:nrow(barcode)) {
    if (sapply(strsplit(barcode$species[i]," "),length) == 2) {
        barcode$bc_sciname[i] <- barcode$species[i]
    } else {
        barcode$bc_sciname[i] <- paste(word(barcode$species[i], 1:2),collapse=" ")
    }
}
# AIS couldn't get this to work in simple ifelse format: barcode %>% mutate(bc_sciname = ifelse( sapply(strsplit(species," "),length) == 2, species, paste(word(species, 1:2),collapse=" ")))

# Join barcode and expert data
bc_exp_taxon_df <- expert %>%
            dplyr::select(individualID, expert_sciname) %>%
            left_join(distinct(barcode, individualID, bc_sciname))

# What percentage of barcode IDs match expert IDs 
bc_exp_taxon_df %>%
  filter(!is.na(bc_sciname)) %>% #samples that expert IDed and where para identified to species
  mutate(same_species = expert_sciname == bc_sciname) %>%
  group_by(expert_sciname) %>%
  summarize(pct_correct = mean(same_species, na.rm = TRUE), 
            tot_correct = sum(same_species, na.rm = TRUE), 
            n = n()) %>%
  arrange(-n) %>%
  data.frame
#100%!


### Now compare barcode/expert to parataxonomist

para <- bet_parataxonomistID %>%
            mutate(para_sciname = scientificName) %>%
            dplyr::select(individualID, morphospeciesID, taxonRank, para_sciname) %>%
            as_tibble

# Join para and expert/barcode data, and evaluate discrepancies in species ids 
taxon_df <- left_join(para, bc_exp_taxon_df)

# para_sciname and expert_sciname don't always match
taxon_df %>%
  filter(taxonRank == "species") %>%
  summarize(mean(para_sciname == expert_sciname, na.rm = TRUE)) 
#0.97 match between specimen identified by expert taxonomist with parataxonomist ID

# Are any species perfectly identified?
taxon_df %>%
  filter(taxonRank == "species", !is.na(expert_sciname)) %>% #samples that expert IDed and where para identified to species 
  mutate(same_species = para_sciname == expert_sciname) %>%
  group_by(para_sciname) %>%
  summarize(pct_correct = mean(same_species, na.rm = TRUE), 
            tot_correct = sum(same_species, na.rm = TRUE), 
            n = n()) %>%
  arrange(-n) %>%
  data.frame
# In all but one case (Cymindis cribricollis), the para and expert IDs were either 0% or 100% matching

# look at some of the discrepancies
taxon_df %>%
  filter(taxonRank == "species", 
         para_sciname != expert_sciname) %>%
  dplyr::select(individualID, para_sciname, expert_sciname) %>%
  arrange(para_sciname) %>%
  data.frame
# Three mismatches checked with taxize pckg had different taxon serial numbers

# Plot discrepancies for samples identified to the species level
taxon_df %>%
  filter(taxonRank == "species", !is.na(expert_sciname)) %>%
  count(para_sciname, expert_sciname) %>%
  arrange(n) %>%
  mutate(discrepancy = para_sciname != expert_sciname) %>%
  ggplot(aes(x = para_sciname, 
             y = expert_sciname, 
             color = discrepancy)) + 
  geom_point(aes(size = n)) + 
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  xlab("Species ID from parataxonomist") + 
  ylab("Species ID from expert taxonomist") + 
  scale_color_manual(values = c("black", "red"))
ggsave(filename = "output/paratax_vs_expertax.png", width = 6, height = 5, dpi = 'retina')


# Plot morphospecies IDs with expert IDs: 
taxon_df %>%
  filter(!is.na(morphospeciesID), !is.na(expert_sciname)) %>% #samples where morphospeciesID and expert ID exists,
  count(morphospeciesID, expert_sciname) %>%
  ggplot(aes(x = fct_reorder(morphospeciesID, n), 
             y = fct_reorder(expert_sciname, n))) + 
  geom_point(aes(size = n)) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
  xlab("Morphospecies from parataxonomist") + 
  ylab("Species ID from expert taxonomist")
# all but one morphospecies map to one expert species ID.
# the D13.2016.MorphBU morphospecies is a mixture of two species

# How many morphospecies occur with each parataxonomist ID?
# note: if the parataxonomist IDed to species, then no morphospecies was assigned
taxon_df %>%
  group_by(para_sciname) %>%
  summarize(morpho_n = n_distinct(morphospeciesID, na.rm = TRUE), 
            n = n()) %>%
  arrange(-n) %>%
  data.frame
# 3 para ID's had more than one morphospecies
# Harpalus sp. (para ID) had 6 morphospecies and Amara sp. (para ID) had 9
# Doesn't seem like using the morphospeciesID is helpful

# Plot discrepancies for all samples, excluding para ID == expert ID
taxon_df %>%
  count(para_sciname, expert_sciname) %>%
  arrange(n) %>%
  mutate(discrepancy = para_sciname != expert_sciname) %>%
  filter(discrepancy == TRUE) %>%
  ggplot(aes(x = para_sciname, 
             y = expert_sciname)) + 
  geom_point(aes(size = n)) + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + 
  xlab("Species ID from parataxonomist") + 
  ylab("Species ID from expert taxonomist") + 
  scale_color_manual(values = c("red"))
  

# Visualize those species with 100% match between para and expert IDs and with n>20
select_spp <- taxon_df %>%
  mutate(same_species = para_sciname == expert_sciname) %>%
  group_by(para_sciname) %>%
  summarize(pct_correct = mean(same_species, na.rm = TRUE), 
            tot_correct = sum(same_species, na.rm = TRUE), #number of individuals for this species where para = expert where expert exists
            n = n()) %>% #number of individuals with para_sciname
  filter(pct_correct == 1, n > 20) %>%
  pull(para_sciname)


# Plot abundance of selected Niwot species over time
taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
    dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% select_spp) %>%
  ggplot() +
  geom_bar(aes(x = collectDate, fill = para_sciname)) +
  facet_wrap(. ~ para_sciname)

# just for the 2 selected species
taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
              dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% c('Carabus taedatus','Cymindis unicolor')) %>%
  ggplot() +
  geom_bar(aes(x = collectDate, fill = para_sciname)) +
  facet_wrap(. ~ para_sciname) +
  theme_bw()
ggsave("output/abund_collectDate_twospp.png", width = 6, height = 5, dpi = 'retina')

# Plot taxa by year (thanks SN)
taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
    dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% select_spp) %>%
  mutate(year = lubridate::year(collectDate), 
         month = lubridate::month(collectDate), 
         day = lubridate::day(collectDate)) %>% 
  group_by(para_sciname, year) %>%
  summarize(n=n())  %>%
  ggplot() +
  geom_line(aes(x = year, y = n, colour = para_sciname, group = para_sciname)) +
  geom_point(aes(x = year, y = n, colour = para_sciname)) +  
  xlab("Collection Year") 
# Pterostichus restrictus came out of nowhere!

# Plot taxa by month through the years
taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
    dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% select_spp) %>%
  mutate(year = lubridate::year(collectDate), 
         month = lubridate::month(collectDate), 
         day = lubridate::day(collectDate)) %>% 
  group_by(para_sciname, year, month) %>%
  summarize(n=n())  %>%
  ggplot() +
  geom_line(aes(x = month, y = n, colour = para_sciname, group = para_sciname)) +
  geom_point(aes(x = month, y = n, colour = para_sciname))  +  
  xlab("Collection Month") +
  facet_grid(year ~ .)
# July has the most abundance, but may have the most collection dates, too

# Plot taxa through time by collection day
taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
    dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% select_spp) %>%
  mutate(year = lubridate::year(collectDate), 
         month = lubridate::month(collectDate), 
         day = lubridate::day(collectDate)) %>% 
  group_by(para_sciname, year, month, day) %>%
  summarize(n=n())  %>%
  ggplot() +
  #geom_line(aes(x = day, y = n, colour = para_sciname, group = para_sciname)) +
  geom_point(aes(x = day, y = n, colour = para_sciname)) +  
  xlab("Collection Day") +
  facet_grid(year ~ month)

# Plot spatial arrangement of plots, labeled with habitat
# add lat and long poitns
bet_fielddata %>% 
  dplyr::select(plotID, nlcdClass, decimalLatitude, decimalLongitude) %>%
  ggplot() +
  geom_point(aes(x = decimalLongitude, y = decimalLatitude, colour = nlcdClass))

# cool variables to look at: bet_fielddata$nativestatuscode
bet_parataxonomistID %>% 
  filter(scientificName %in% select_spp) %>%
  dplyr::select(scientificName, nativeStatusCode) %>%
  summarize(mean(nativeStatusCode == "N") )
# All species are classified as native


### Modeling EDA
# What variables might be related to carabid abundance?

carabid_df <- taxon_df %>% 
  full_join(bet_parataxonomistID %>% 
    dplyr::select(individualID, plotID , trapID , collectDate)) %>% 
  filter(para_sciname %in% select_spp) %>%
  left_join(distinct(bet_fielddata %>%
              dplyr::select(plotID, nlcdClass, decimalLatitude, decimalLongitude, elevation))) %>%
  mutate(year = lubridate::year(collectDate), 
         month = lubridate::month(collectDate), 
         day = lubridate::day(collectDate)) %>%
       mutate_at(c("morphospeciesID", "taxonRank", "para_sciname", "expert_sciname", "bc_sciname", "plotID", "trapID", "nlcdClass"), funs(factor(.)))

# Group data by plot and species and summarize by species abundance
model_test_abund <- carabid_df %>%
  group_by(plotID, trapID, para_sciname, nlcdClass) %>% #para_sciname
  summarize(sp_abund = n() ) %>%
  arrange(plotID) %>%
  data.frame()

model_test_comp <- carabid_df %>%
  group_by(plotID, nlcdClass) %>%
  summarize(sp_comp = n_distinct(para_sciname, na.rm = TRUE) ) %>%
  arrange(plotID) %>%
  data.frame()
# AIS later, try grouping by collection date and trapID

# Is habitat type a good predictor for species abundance?
# Constant
fit_abund_0 <- glm(formula = sp_abund ~ 1, 
    family = poisson(link="log"),
      data = model_test_abund)
# With habitat type
fit_abund_1 <- glm(formula = sp_abund ~ nlcdClass, 
    family = poisson(link="log"),
      data = model_test_abund)
fit_abund_2 <- glmer(formula = sp_abund ~ nlcdClass + para_sciname + (1|plotID) , 
    family = poisson(link="log"),
      data = model_test_abund)
display(fit_abund_0)
display(fit_abund_1)
display(fit_abund_2)
anova(fit_abund_0, fit_abund_1, fit_abund_2)

# Is habitat type a good predictor for species composition?
# Constant
fit_comp_0 <- glm(formula = sp_comp ~ 1, 
    family = poisson(link="log"),
      data = model_test_comp)
# With habitat type
fit_comp_1 <- glm(formula = sp_comp ~ nlcdClass, 
    family = poisson(link="log"),
      data = model_test_comp)
display(fit_comp_0)
display(fit_comp_1)






### SCRATCH ###
# # How to decide on the final species ID to use?
# # Pseudocode
# # for individuals that have expertID,
#   # If paraID matches expertID
#     # then assign expertID to all individuals in para with that paraID (supported by 100% match of expert and barcode ID)
#   # else if expertID does NOT match paraID
#     # then still take expertID?
# # for individuals that do not have expertID
#   # if their paraID was matched to an expertID elsewhere in the df, take the expertID
#   # if their paraID has no expert match, then assign NA to final_sciname and don't get analyzed?
# 
# matched_species <- taxon_df %>%
#   filter(!is.na(expert_sciname)) %>%
#   select(para_sciname, expert_sciname) %>%
#   distinct() %>%
#   arrange(expert_sciname) %>%
#   data.frame
# 
# taxon_df <- taxon_df %>% 
#   mutate(same_species = para_sciname == expert_sciname, 
#          final_sciname = NA) 
# # for each row where same_species is T, use expertID on all other rows with matching paraIDs
# for (i in 1:nrow(taxon_df)) { 
#     if (!is.na(taxon_df$expert_sciname[i])) {
#         taxon_df$final_sciname[i] <- taxon_df$expert_sciname[i]
#     } else {
#         if (taxon_df$para_sciname[i] %in% matched_species$para_sciname) {
#             # if their paraID was matched to an expertID elsewhere in the df, take the expertID. Here we risk the chance that the parataxonomist identified two different species as one (e.g. para ID for Amara sp. matches 6 expert IDs)
#         }
#     }
# }