What things are misidentified as large milkweed bug?

General
4

It’s not the greatest code I’ve ever written, it’s in R, and it works in the current form for any species-level question just by changing the taxon_name and the taxon_id at the top of the script. It only goes through genus-level differences to species, no grouping by higher taxonomic levels. It would probably be pretty straight forward to change it up to other levels though. The large milkweed bug one will be ~marginally~ different because it’s supplemented by our specific corrections.

Tulip poplar:

tulip_tree
tulip_tree1092×668 85.5 KB

Honey bee:

honey bee
honey bee1092×668 86.7 KB

#install.packages("tidyverse", "devtools", "networkD3")
#library(devtools)
#devtools::install_github("hrbrmstr/curlconverter")
library(curlconverter)
library(tidyverse)
library(networkD3)
taxon_id <- 47219
taxon_name <- "Apis mellifera"

####API Call####
api <- paste("curl -X GET --header 'Accept: application/json' 
 'http://api.inaturalist.org/v1/identifications/similar_species?    is_change=false&current=true&order=desc&order_by=created_at&taxon_id=", taxon_id, "'", sep="")
my_ip <- straighten(api) %>% 
  make_req()

dat <- content(my_ip[[1]](), as="parsed")
links <- data.frame(target = rep(NA, dat$total_results), value = rep(NA, dat$total_results))
for(i in 1:dat$total_results){
  links$target[i] <- dat$results[[i]]$taxon$name
  links$value[i] <- dat$results[[i]]$count
}

####Formatting####
links %>% 
  separate(target, c("genus", "species")) %>% 
  group_by(genus) %>%
  summarise(n = n(), value = sum(value)) %>%
  filter(n > 1) %>% 
  mutate(target = genus)%>%
  select(-n, -genus) -> genera
genera$source <- rep(taxon_name, nrow(genera))
links$source <- rep(taxon_name, nrow(links))

links %>% 
  separate(target, c("genus", "species"), remove = F) -> links
links$source[links$genus %in% genera$target] <- links$genus[links$genus %in% genera$target]
links <- select(links, -genus, -species)
links <- bind_rows(links, genera)

####SANKEY####
nodes <- data.frame(
  name=c(as.character(links$source), 
         as.character(links$target)) %>% unique()
)

# With networkD3, connection must be provided using id, not using real name like in the links dataframe.. So we need to reformat it.
links$IDsource <- match(links$source, nodes$name)-1 
links$IDtarget <- match(links$target, nodes$name)-1

# Add a 'group' column to each node. Here I decide to put all of them in the same group to make them grey
nodes$group <- as.factor(c("my_unique_group"))

# Make the Network
p <- sankeyNetwork(Links = links,
                   Nodes = nodes,
                   Source = "IDsource",
                   Target = "IDtarget",
                   Value = "value",
                   NodeID = "name", 
                   sinksRight=F,
                   fontSize = 20,
                   iterations = 23,
                   nodeWidth = 5)
p
16 Likes