In HMB3/nenswniche: Rapidly estimate species ranges and intersect with other layers
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TARGET INSECT TAXA LISTS =============================================================
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This code prepares all the data and code needed for the analysis of inverts habitat after the 2019-2020 fires ::
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To install, run :
library(readxl)
library(tidyverse)
library(R.utils)
##
completeFun <- function(data, desiredCols) {
completeVec <- complete.cases(data[, desiredCols])
return(data[completeVec, ])
}
read_excel_allsheets <- function(filename, tibble = FALSE) {
# I prefer straight data.frames
# but if you like tidyverse tibbles (the default with read_excel)
# then just pass tibble = TRUE
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
## Set variables
write_invert_data <- FALSE
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1). TARGET INSECT TAXALISTS =============================================================
The backbone of the R workflow is a list of (taxonomically Ridgey-Didge!) Taxa names
that we supply. The analysis is designed to process data for one taxa at a time,
allowing taxa results to be updated as required. Unfortunately, Australia's insects
are not very well sampled...so we can analyse at the genus and family level.
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The site data comes from the PBI database :
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PBI_site_data <- read_excel_allsheets('./data/Taxonomy/HETEROPTERA AUSTRALIA 26 March 2020.xlsx')
## Insect species df
target.insect.spp.df = read_excel('./data/Taxonomy/Habitat_fire_recovery_invertebrate_species.xlsx',
sheet = 'Insect species')
## Insect species
target.insect.spp = target.insect.spp.df$searchTaxon %>% unique() %>% str_trim() %>% .[!is.na(.)]
## Insect genera
target.insect.genera = target.insect.spp.df$Genus %>% unique() %>% str_trim() %>% .[!is.na(.)]
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2). STATE INSECT TAXALISTS =============================================================
The backbone of the R workflow is a list of (taxonomically Ridgey-Didge!) Taxa names
that we supply. The analysis is designed to process data for one taxa at a time,
allowing taxa results to be updated as required. Unfortunately, Australia's insects
are not very well sampled...so we can analyse at the genus and family level.
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The site data comes from the PBI database :
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## Common cols between SITES and ALA
site_cols <- c("genus",
"species",
"family",
"Host_Genus",
"Host_species",
"plantTaxon",
"lat",
"lon",
"country",
"state",
"locality",
"institutionCode",
"basisOfRecord")
## Read in all the bugs for the projects
## QLD SITES ----
QLD.insects = PBI_site_data[["QLD BUGS"]]
QLD.insect.sites.ALA <- QLD.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
## What are these data sets? Are they the field data from 1993?
names(QLD.insects)
QLD.insect.genera <- QLD.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
QLD.insect.family <- QLD.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
QLD.insect.spp <- QLD.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
QLD.insect.plants.genus <- QLD.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
QLD.insect.plants <- QLD.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## NSW SITES ----
NSW.insects = PBI_site_data[["NSW BUGS"]]
##
NSW.insect.sites.ALA <- NSW.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
NSW.insect.genera <- NSW.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
NSW.insect.family <- NSW.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
NSW.insect.spp <- NSW.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
NSW.insect.plants.genus <- NSW.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
NSW.insect.plants <- NSW.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## VIC SITES ----
VIC.insects = PBI_site_data[["VIC BUGS"]]
##
VIC.insect.sites.ALA <- VIC.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
VIC.insect.genera <- VIC.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
VIC.insect.family <- VIC.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
VIC.insect.spp <- VIC.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
VIC.insect.plants.genus <- VIC.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
VIC.insect.plants <- VIC.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## TAS SITES ----
TAS.insects = PBI_site_data[["TAS BUGS"]]
##
TAS.insect.sites.ALA <- TAS.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
TAS.insect.genera <- TAS.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
TAS.insect.family <- TAS.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
TAS.insect.spp <- TAS.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
TAS.insect.plants.genus <- TAS.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
TAS.insect.plants <- TAS.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## SA SITES ----
SA.insects = PBI_site_data[["SA BUGS"]]
##
SA.insect.sites.ALA <- SA.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
SA.insect.genera <- SA.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
SA.insect.family <- SA.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
SA.insect.spp <- SA.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
SA.insect.plants.genus <- SA.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
SA.insect.plants <- SA.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## WA SITES ----
WA.insects = PBI_site_data[["WA BUGS"]]
##
WA.insect.sites.ALA <- WA.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
WA.insect.genera <- WA.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
WA.insect.family <- WA.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
WA.insect.spp <- WA.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
WA.insect.plants.genus <- WA.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
WA.insect.plants <- WA.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## NT SITES ----
NT.insects = PBI_site_data[["NT BUGS"]]
##
NT.insect.sites.ALA <- NT.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
NT.insect.genera <- NT.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
NT.insect.family <- NT.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
NT.insect.spp <- NT.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
##
NT.insect.plants.genus <- NT.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
NT.insect.plants <- NT.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## ACT SITES ----
ACT.insects = PBI_site_data[["ACT BUGS"]]
##
ACT.insect.sites.ALA <- ACT.insects %>%
## Now clean up the data so it can be combined with the ALA
mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::rename(genus = Genus,
locality = Locality,
country = Country,
family = Family,
lat = Lat,
lon = Lon,
institutionCode = Inst_Code,
recordedBy = Collector,
state = State_Prov,
basisOfRecord = Coll_Method) %>%
mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
## Change this to the order of the clean columns
dplyr::select(searchTaxon, one_of(site_cols))
##
ACT.insect.genera <- ACT.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus
ACT.insect.family <- ACT.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family
ACT.insect.spp <- ACT.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon
##
ACT.insect.plants.genus <- ACT.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)]
ACT.insect.plants <- ACT.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>%
dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>%
gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique()
## 3). TOTAL TAXA LISTS =============================================================================
##
all.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp,
TAS.insect.spp, TAS.insect.spp, NT.insect.spp,
ACT.insect.spp, SA.insect.spp) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
##
east.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, ACT.insect.spp) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
## Combine all the insect genera into one list
all.insect.genera <- c(QLD.insect.genera, NSW.insect.genera, VIC.insect.genera,
TAS.insect.genera, TAS.insect.genera, NT.insect.genera,
ACT.insect.genera, SA.insect.genera) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
## Combine all the insect genera into one list
all.insect.families <- c(QLD.insect.family, NSW.insect.family, VIC.insect.family,
TAS.insect.family, TAS.insect.family, NT.insect.family,
ACT.insect.family, SA.insect.family) %>% unique() %>% str_trim() %>% .[!is.na(.)] #%>% toupper()
all.insect.plant.spp <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants,
TAS.insect.plants, TAS.insect.plants, NT.insect.plants,
ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] %>% .[. != ""]
all.insect.plant.genera <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants,
TAS.insect.plants, TAS.insect.plants, NT.insect.plants,
ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)]
if(write_invert_data) {
## Save taxa out for manual cleaning
# insect_species_data <- as_tibble(all.insect.spp) %>% dplyr::rename(searchTaxon = value)
# insect_genus_data <- as_tibble(all.insect.genera) %>% dplyr::rename(searchTaxon = value)
# plant_species_data <- as_tibble(all.insect.plant.spp) %>% dplyr::rename(searchTaxon = value)
# write_csv(insect_species_data, './data/Taxonomy/insect_species_data.csv')
# write_csv(plant_species_data, './data/Taxonomy/insect_plant_data.csv')
}
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3). OVERALL INVERT TAXA LISTS =============================================================
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Now combine all the state PBI data into single lists :
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##
all.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp,
TAS.insect.spp, TAS.insect.spp, NT.insect.spp,
ACT.insect.spp, SA.insect.spp) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
##
east.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, ACT.insect.spp) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
## Combine all the insect genera into one list
all.insect.genera <- c(QLD.insect.genera, NSW.insect.genera, VIC.insect.genera,
TAS.insect.genera, TAS.insect.genera, NT.insect.genera,
ACT.insect.genera, SA.insect.genera) %>% unique() %>%
str_trim() %>% textclean::strip() %>% capitalize() %>%
gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>%
gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>%
gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>%
gsub(' msp', '', .,) %>%
gsub(' C', '', .,) %>% str_trim() %>% unique()
## Combine all the insect genera into one list
all.insect.families <- c(QLD.insect.family, NSW.insect.family, VIC.insect.family,
TAS.insect.family, TAS.insect.family, NT.insect.family,
ACT.insect.family, SA.insect.family) %>% unique() %>% str_trim() %>% .[!is.na(.)] #%>% toupper()
all.insect.plant.spp <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants,
TAS.insect.plants, TAS.insect.plants, NT.insect.plants,
ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] %>% .[. != ""]
all.insect.plant.genera <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants,
TAS.insect.plants, TAS.insect.plants, NT.insect.plants,
ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)]
## Save taxa out for manual cleaning
# insect_species_data <- as_tibble(all.insect.spp) %>% dplyr::rename(searchTaxon = value)
# insect_genus_data <- as_tibble(all.insect.genera) %>% dplyr::rename(searchTaxon = value)
# plant_species_data <- as_tibble(all.insect.plant.spp) %>% dplyr::rename(searchTaxon = value)
# write_csv(insect_species_data, './data/Taxonomy/insect_species_data.csv')
# write_csv(plant_species_data, './data/Taxonomy/insect_plant_data.csv')
\
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4). OVERALL INVERT TAXA LISTS =============================================================
\
Now combine all the state PBI data into single lists :
\
## Combine all the site data into one table
all_insect_site_df_species <- bind_rows(QLD.insect.sites.ALA,
NSW.insect.sites.ALA,
VIC.insect.sites.ALA,
SA.insect.sites.ALA,
WA.insect.sites.ALA,
NT.insect.sites.ALA,
ACT.insect.sites.ALA) %>%
## Remove the NA coordinates, and add a 'SOURCE' column
completeFun(., c('lat', 'lon')) %>%
filter(lon < 180 & lat > -90) %>%
mutate(SOURCE = 'SITE')
## Genera
all_insect_site_df_genera <- all_insect_site_df_species %>%
dplyr::select(-searchTaxon) %>%
dplyr::rename(searchTaxon = genus) %>% dplyr::select(searchTaxon, everything())
## Families
all_insect_site_df_families <- all_insect_site_df_species %>%
dplyr::select(-searchTaxon) %>%
dplyr::rename(searchTaxon = family) %>% dplyr::select(searchTaxon, everything())
## Target families
target.insect.families <- all_insect_site_df_species %>%
.[.$searchTaxon %in% target.insect.spp, ] %>% .$family %>% unique() %>% str_trim() %>% .[!is.na(.)]
## Target host plants
target.host.plants.df <- all_insect_site_df_species %>%
.[.$searchTaxon %in% target.insect.spp, ] %>% distinct(searchTaxon, .keep_all = TRUE)
## Target host plants=
target.host.plants <- all_insect_site_df_species %>%
.[.$searchTaxon %in% target.insect.spp, ] %>% .$plantTaxon %>%
unique() %>% str_trim() %>%
gsub('sp.', '', .,) %>% gsub(' NA', '', .,) %>% gsub('NA ', '', .,) %>% gsub("NA", '', .,) %>%
.[!is.na(.)] %>% .[. != ""]
## Target Insects and their host plants
target_insects_host_plants <- all_insect_site_df_species %>%
## This target list might not have character strings matching the site data
## Can't just trust the list matching without checking
.[.$searchTaxon %in% target.insect.spp, ] %>%
## Clean plant Taxon
mutate(plantTaxon = str_trim(plantTaxon),
plantTaxon = gsub(' NA', '', plantTaxon),
plantTaxon = gsub('sp.', '', plantTaxon),
plantTaxon = gsub('NA ', '', plantTaxon),
plantTaxon = gsub("NA", '', plantTaxon)) %>%
.[!duplicated(.[,c('searchTaxon')]),] %>%
dplyr::select(searchTaxon, plantTaxon)
## How big is this df?
nrow(all_insect_site_df_species)
length(unique(all_insect_site_df_species$searchTaxon))
length(unique(all_insect_site_df_species$genus))
length(unique(all_insect_site_df_species$family))
## Check the records for a subset of taxa in the final data
# SDM.SPAT.OCC.BG.GDA.DF = readRDS('./output/results/SDM_SPAT_OCC_BG_GDA_ALL_TAXA.rds') %>% as.data.frame()
if(write_invert_data) {
## Output combined data sets to csv
# write_csv(all_insect_site_df_species, './output/veg_climate_topo_maxent/Habitat_suitability/AUS_ALL_INSECT_SITE_DATA.csv')
# write_csv(SDM.SPAT.OCC.BG.GDA.DF, './output/veg_climate_topo_maxent/Habitat_suitability/AUS_TARGET_INSECT_SDM_DATA.csv')
}
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HMB3/nenswniche documentation built on Jan. 31, 2023, 11:46 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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TARGET INSECT TAXA LISTS =============================================================
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This code prepares all the data and code needed for the analysis of inverts habitat after the 2019-2020 fires ::
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To install, run :
library(readxl) library(tidyverse) library(R.utils) ## completeFun <- function(data, desiredCols) { completeVec <- complete.cases(data[, desiredCols]) return(data[completeVec, ]) } read_excel_allsheets <- function(filename, tibble = FALSE) { # I prefer straight data.frames # but if you like tidyverse tibbles (the default with read_excel) # then just pass tibble = TRUE sheets <- readxl::excel_sheets(filename) x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X)) if(!tibble) x <- lapply(x, as.data.frame) names(x) <- sheets x } ## Set variables write_invert_data <- FALSE
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1). TARGET INSECT TAXALISTS =============================================================
The backbone of the R workflow is a list of (taxonomically Ridgey-Didge!) Taxa names that we supply. The analysis is designed to process data for one taxa at a time, allowing taxa results to be updated as required. Unfortunately, Australia's insects are not very well sampled...so we can analyse at the genus and family level.
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The site data comes from the PBI database :
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PBI_site_data <- read_excel_allsheets('./data/Taxonomy/HETEROPTERA AUSTRALIA 26 March 2020.xlsx') ## Insect species df target.insect.spp.df = read_excel('./data/Taxonomy/Habitat_fire_recovery_invertebrate_species.xlsx', sheet = 'Insect species') ## Insect species target.insect.spp = target.insect.spp.df$searchTaxon %>% unique() %>% str_trim() %>% .[!is.na(.)] ## Insect genera target.insect.genera = target.insect.spp.df$Genus %>% unique() %>% str_trim() %>% .[!is.na(.)]
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2). STATE INSECT TAXALISTS =============================================================
The backbone of the R workflow is a list of (taxonomically Ridgey-Didge!) Taxa names that we supply. The analysis is designed to process data for one taxa at a time, allowing taxa results to be updated as required. Unfortunately, Australia's insects are not very well sampled...so we can analyse at the genus and family level.
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The site data comes from the PBI database :
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## Common cols between SITES and ALA site_cols <- c("genus", "species", "family", "Host_Genus", "Host_species", "plantTaxon", "lat", "lon", "country", "state", "locality", "institutionCode", "basisOfRecord") ## Read in all the bugs for the projects ## QLD SITES ---- QLD.insects = PBI_site_data[["QLD BUGS"]] QLD.insect.sites.ALA <- QLD.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## What are these data sets? Are they the field data from 1993? names(QLD.insects) QLD.insect.genera <- QLD.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus QLD.insect.family <- QLD.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family QLD.insect.spp <- QLD.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon QLD.insect.plants.genus <- QLD.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] QLD.insect.plants <- QLD.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## NSW SITES ---- NSW.insects = PBI_site_data[["NSW BUGS"]] ## NSW.insect.sites.ALA <- NSW.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## NSW.insect.genera <- NSW.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus NSW.insect.family <- NSW.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family NSW.insect.spp <- NSW.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon NSW.insect.plants.genus <- NSW.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] NSW.insect.plants <- NSW.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## VIC SITES ---- VIC.insects = PBI_site_data[["VIC BUGS"]] ## VIC.insect.sites.ALA <- VIC.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) VIC.insect.genera <- VIC.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus VIC.insect.family <- VIC.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family VIC.insect.spp <- VIC.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon VIC.insect.plants.genus <- VIC.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] VIC.insect.plants <- VIC.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## TAS SITES ---- TAS.insects = PBI_site_data[["TAS BUGS"]] ## TAS.insect.sites.ALA <- TAS.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## TAS.insect.genera <- TAS.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus TAS.insect.family <- TAS.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family TAS.insect.spp <- TAS.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon TAS.insect.plants.genus <- TAS.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] TAS.insect.plants <- TAS.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## SA SITES ---- SA.insects = PBI_site_data[["SA BUGS"]] ## SA.insect.sites.ALA <- SA.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## SA.insect.genera <- SA.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus SA.insect.family <- SA.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family SA.insect.spp <- SA.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon SA.insect.plants.genus <- SA.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] SA.insect.plants <- SA.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## WA SITES ---- WA.insects = PBI_site_data[["WA BUGS"]] ## WA.insect.sites.ALA <- WA.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## WA.insect.genera <- WA.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus WA.insect.family <- WA.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family WA.insect.spp <- WA.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon WA.insect.plants.genus <- WA.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] WA.insect.plants <- WA.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## NT SITES ---- NT.insects = PBI_site_data[["NT BUGS"]] ## NT.insect.sites.ALA <- NT.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## NT.insect.genera <- NT.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus NT.insect.family <- NT.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family NT.insect.spp <- NT.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon ## NT.insect.plants.genus <- NT.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] NT.insect.plants <- NT.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## ACT SITES ---- ACT.insects = PBI_site_data[["ACT BUGS"]] ## ACT.insect.sites.ALA <- ACT.insects %>% ## Now clean up the data so it can be combined with the ALA mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::rename(genus = Genus, locality = Locality, country = Country, family = Family, lat = Lat, lon = Lon, institutionCode = Inst_Code, recordedBy = Collector, state = State_Prov, basisOfRecord = Coll_Method) %>% mutate(plantTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% ## Change this to the order of the clean columns dplyr::select(searchTaxon, one_of(site_cols)) ## ACT.insect.genera <- ACT.insects %>% dplyr::select(Genus) %>% distinct() %>% .$Genus ACT.insect.family <- ACT.insects %>% dplyr::select(Family) %>% distinct() %>% .$Family ACT.insect.spp <- ACT.insects %>% mutate(searchTaxon = paste(Genus, species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon ## ACT.insect.plants.genus <- ACT.insects %>% dplyr::select(Host_Genus) %>% distinct() %>% .$Host_Genus %>% .[!is.na(.)] ACT.insect.plants <- ACT.insects %>% mutate(searchTaxon = paste(Host_Genus, Host_species, sep = " ")) %>% dplyr::select(searchTaxon) %>% distinct() %>% .$searchTaxon %>% gsub(' NA', '', .,) %>% gsub(' sp.', '', .,) %>% gsub('NA', '', .,) %>% unique() ## 3). TOTAL TAXA LISTS ============================================================================= ## all.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, TAS.insect.spp, TAS.insect.spp, NT.insect.spp, ACT.insect.spp, SA.insect.spp) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## east.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, ACT.insect.spp) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## Combine all the insect genera into one list all.insect.genera <- c(QLD.insect.genera, NSW.insect.genera, VIC.insect.genera, TAS.insect.genera, TAS.insect.genera, NT.insect.genera, ACT.insect.genera, SA.insect.genera) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## Combine all the insect genera into one list all.insect.families <- c(QLD.insect.family, NSW.insect.family, VIC.insect.family, TAS.insect.family, TAS.insect.family, NT.insect.family, ACT.insect.family, SA.insect.family) %>% unique() %>% str_trim() %>% .[!is.na(.)] #%>% toupper() all.insect.plant.spp <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants, TAS.insect.plants, TAS.insect.plants, NT.insect.plants, ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] %>% .[. != ""] all.insect.plant.genera <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants, TAS.insect.plants, TAS.insect.plants, NT.insect.plants, ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] if(write_invert_data) { ## Save taxa out for manual cleaning # insect_species_data <- as_tibble(all.insect.spp) %>% dplyr::rename(searchTaxon = value) # insect_genus_data <- as_tibble(all.insect.genera) %>% dplyr::rename(searchTaxon = value) # plant_species_data <- as_tibble(all.insect.plant.spp) %>% dplyr::rename(searchTaxon = value) # write_csv(insect_species_data, './data/Taxonomy/insect_species_data.csv') # write_csv(plant_species_data, './data/Taxonomy/insect_plant_data.csv') }
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3). OVERALL INVERT TAXA LISTS =============================================================
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Now combine all the state PBI data into single lists :
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## all.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, TAS.insect.spp, TAS.insect.spp, NT.insect.spp, ACT.insect.spp, SA.insect.spp) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## east.insect.spp <- c(QLD.insect.spp, NSW.insect.spp, VIC.insect.spp, ACT.insect.spp) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## Combine all the insect genera into one list all.insect.genera <- c(QLD.insect.genera, NSW.insect.genera, VIC.insect.genera, TAS.insect.genera, TAS.insect.genera, NT.insect.genera, ACT.insect.genera, SA.insect.genera) %>% unique() %>% str_trim() %>% textclean::strip() %>% capitalize() %>% gsub('Sp ', '', .,) %>% gsub('Spbbcqld Msp', '', .,) %>% gsub('Spbbqkn Msp', '', .,) %>% gsub(".* Msp","", .,) %>% gsub('Spnsp ', '', .,) %>% gsub(' sp', '', .,) %>% gsub(' msp', '', .,) %>% gsub(' C', '', .,) %>% str_trim() %>% unique() ## Combine all the insect genera into one list all.insect.families <- c(QLD.insect.family, NSW.insect.family, VIC.insect.family, TAS.insect.family, TAS.insect.family, NT.insect.family, ACT.insect.family, SA.insect.family) %>% unique() %>% str_trim() %>% .[!is.na(.)] #%>% toupper() all.insect.plant.spp <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants, TAS.insect.plants, TAS.insect.plants, NT.insect.plants, ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] %>% .[. != ""] all.insect.plant.genera <- c(QLD.insect.plants, NSW.insect.plants, VIC.insect.plants, TAS.insect.plants, TAS.insect.plants, NT.insect.plants, ACT.insect.plants, SA.insect.plants) %>% unique() %>% str_trim() %>% .[!is.na(.)] ## Save taxa out for manual cleaning # insect_species_data <- as_tibble(all.insect.spp) %>% dplyr::rename(searchTaxon = value) # insect_genus_data <- as_tibble(all.insect.genera) %>% dplyr::rename(searchTaxon = value) # plant_species_data <- as_tibble(all.insect.plant.spp) %>% dplyr::rename(searchTaxon = value) # write_csv(insect_species_data, './data/Taxonomy/insect_species_data.csv') # write_csv(plant_species_data, './data/Taxonomy/insect_plant_data.csv')
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4). OVERALL INVERT TAXA LISTS =============================================================
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Now combine all the state PBI data into single lists :
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## Combine all the site data into one table all_insect_site_df_species <- bind_rows(QLD.insect.sites.ALA, NSW.insect.sites.ALA, VIC.insect.sites.ALA, SA.insect.sites.ALA, WA.insect.sites.ALA, NT.insect.sites.ALA, ACT.insect.sites.ALA) %>% ## Remove the NA coordinates, and add a 'SOURCE' column completeFun(., c('lat', 'lon')) %>% filter(lon < 180 & lat > -90) %>% mutate(SOURCE = 'SITE') ## Genera all_insect_site_df_genera <- all_insect_site_df_species %>% dplyr::select(-searchTaxon) %>% dplyr::rename(searchTaxon = genus) %>% dplyr::select(searchTaxon, everything()) ## Families all_insect_site_df_families <- all_insect_site_df_species %>% dplyr::select(-searchTaxon) %>% dplyr::rename(searchTaxon = family) %>% dplyr::select(searchTaxon, everything()) ## Target families target.insect.families <- all_insect_site_df_species %>% .[.$searchTaxon %in% target.insect.spp, ] %>% .$family %>% unique() %>% str_trim() %>% .[!is.na(.)] ## Target host plants target.host.plants.df <- all_insect_site_df_species %>% .[.$searchTaxon %in% target.insect.spp, ] %>% distinct(searchTaxon, .keep_all = TRUE) ## Target host plants= target.host.plants <- all_insect_site_df_species %>% .[.$searchTaxon %in% target.insect.spp, ] %>% .$plantTaxon %>% unique() %>% str_trim() %>% gsub('sp.', '', .,) %>% gsub(' NA', '', .,) %>% gsub('NA ', '', .,) %>% gsub("NA", '', .,) %>% .[!is.na(.)] %>% .[. != ""] ## Target Insects and their host plants target_insects_host_plants <- all_insect_site_df_species %>% ## This target list might not have character strings matching the site data ## Can't just trust the list matching without checking .[.$searchTaxon %in% target.insect.spp, ] %>% ## Clean plant Taxon mutate(plantTaxon = str_trim(plantTaxon), plantTaxon = gsub(' NA', '', plantTaxon), plantTaxon = gsub('sp.', '', plantTaxon), plantTaxon = gsub('NA ', '', plantTaxon), plantTaxon = gsub("NA", '', plantTaxon)) %>% .[!duplicated(.[,c('searchTaxon')]),] %>% dplyr::select(searchTaxon, plantTaxon) ## How big is this df? nrow(all_insect_site_df_species) length(unique(all_insect_site_df_species$searchTaxon)) length(unique(all_insect_site_df_species$genus)) length(unique(all_insect_site_df_species$family)) ## Check the records for a subset of taxa in the final data # SDM.SPAT.OCC.BG.GDA.DF = readRDS('./output/results/SDM_SPAT_OCC_BG_GDA_ALL_TAXA.rds') %>% as.data.frame() if(write_invert_data) { ## Output combined data sets to csv # write_csv(all_insect_site_df_species, './output/veg_climate_topo_maxent/Habitat_suitability/AUS_ALL_INSECT_SITE_DATA.csv') # write_csv(SDM.SPAT.OCC.BG.GDA.DF, './output/veg_climate_topo_maxent/Habitat_suitability/AUS_TARGET_INSECT_SDM_DATA.csv') }
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