In HMB3/nenswniche: Rapidly estimate species ranges and intersect with other layers

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1). CREATE OCCURRENCE LISTS ===========================================================================================

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Lists needed for occurrence data

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To install, run :

## GBIF columns to keep ----
gbif_keep <- c(## TAXONOMY
  "searchTaxon",
  "species",
  "scientificName",
  "taxonRank",
  "taxonKey",
  "genus",
  "family",

  ## CULTIVATION
  "cloc",
  "basisOfRecord",
  "locality",
  "establishmentMeans",
  "institutionCode",
  "datasetName",
  "habitat",
  "eventRemarks",

  ## RECORD ID
  "recordedBy",
  "identifiedBy",

  ## PLACE/TIME
  "lat",
  "lon",
  "decimalLatitude",
  "decimalLongitude",
  "country",
  "coordinateUncertaintyInMeters",
  "geodeticDatum",
  "year",
  "month",
  "day",
  "eventID")


## ALA Enviro cols ----
## Createa a table of all the ALA columns
# ALA_columns     <- ala_fields("occurrence_stored",as_is = TRUE)$description[231:395]
# names(ALA)[58:215]


## ALA columns to keep ----
ALA_keep <- c(## TAXONOMY
  "searchTaxon",
  "scientificName",
  "scientificNameOriginal",
  "species",
  "taxonRank",
  "rank",
  "genus",
  "family",

  ## CULTIVATION
  "occCultivatedEscapee",
  "basisOfRecord",
  "locality",
  "establishmentMeans",
  "institutionCode",
  "datasetName",
  "habitat",
  "eventRemarks",
  "taxonomicQuality",

  ## RECORD ID
  "recordedBy",
  "id",
  # "catalogNumber",
  "identificationID",
  "identifiedBy",
  "occurrenceID",
  "basisOfRecord",
  "institutionCode",

  ## PLACE/TIME
  "lat",
  "lon",
  "coordinateUncertaintyInMetres",
  "coordinateUncertaintyInMeters",
  "country",
  "state",
  "IBRA7Regions",
  "IBRA7Subregions",
  "localGovernmentAreas",
  "locality",
  "geodeticDatum",
  "year",
  "month",
  "day",
  # "eventDate",
  "eventID",

  ## Quality
  "zeroCoordinates",
  "zeroLatitude",
  "zeroLongitude",
  "coordinatesCentreOfCountry",
  "countryCoordinateMismatch",
  "invertedCoordinates",
  "inferredDuplicateRecord")

## Enviro
# env_cols <- names(ALA)[58:215]


common_cols <- c('searchTaxon',     
                 'scientificName', 
                 'species',         
                 'genus',           
                 'family',          
                 'basisOfRecord',   
                 'locality',       
                 'institutionCode', 
                 'id',              
                 'catalogNumber',   
                 'lat',             
                 'lon',             
                 'country',         
                 'year',           
                 'month',           
                 'eventDate')

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2). CREATE RASTER LISTS =============================================================

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Lists needed for raster analyses.

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## Create the variables needed to access current environmental conditions + their names in the functions
## Names of all the worldclim variables used to extract the raster data
env_variables = c("Annual_mean_temp",
                  "Mean_diurnal_range",
                  "Isothermality",
                  "Temp_seasonality",
                  "Max_temp_warm_month",
                  "Min_temp_cold_month",
                  "Temp_annual_range",
                  "Mean_temp_wet_qu",
                  "Mean_temp_dry_qu",
                  "Mean_temp_warm_qu",
                  "Mean_temp_cold_qu",

                  "Annual_precip",
                  "Precip_wet_month",
                  "Precip_dry_month",
                  "Precip_seasonality",
                  "Precip_wet_qu",
                  "Precip_dry_qu",
                  "Precip_warm_qu",
                  "Precip_col_qu")

bioclim_variables = c('bio_01',
                      'bio_02',
                      'bio_03',
                      'bio_04',
                      'bio_05',
                      'bio_06',
                      'bio_07',
                      'bio_08',
                      'bio_09',
                      'bio_10',
                      'bio_11',

                      ## Rainfall
                      'bio_12',
                      'bio_13',
                      'bio_14',
                      'bio_15',
                      'bio_16',
                      'bio_17',
                      'bio_18',
                      'bio_19')


## Names of the sdm data table ---
sdm_table_vars <- c('searchTaxon', 'lon', 'lat', 'SOURCE',

                    'Annual_mean_temp',     'Mean_diurnal_range',  'Isothermality', 'Temp_seasonality',
                    'Max_temp_warm_month',  'Min_temp_cold_month', 'Temp_annual_range', 'Mean_temp_wet_qu',
                    'Mean_temp_dry_qu',     'Mean_temp_warm_qu',   'Mean_temp_cold_qu',

                    'Annual_precip',        'Precip_wet_month',    'Precip_dry_month',  'Precip_seasonality',
                    'Precip_wet_qu',        'Precip_dry_qu',       'Precip_warm_qu',    'Precip_col_qu')


## Names of the best 15 worldclim predictors ----
## i.e. 'backwards selected' predictors
bs_predictors <- c("Annual_mean_temp",    "Mean_diurnal_range",  "Isothermality",      "Temp_seasonality",
                   "Max_temp_warm_month", "Min_temp_cold_month", "Temp_annual_range",
                   "Mean_temp_warm_qu",   "Mean_temp_cold_qu",

                   "Annual_precip",       "Precip_wet_month",   "Precip_dry_month",    "Precip_seasonality",
                   "Precip_wet_qu",       "Precip_dry_qu")




## Just get the 6 models picked by CSIRO for Australia, for 2030, 2050 and 2070
## See the publication for why we choose this
scen_2030 = c("mc85bi30", "no85bi30", "ac85bi30", "cc85bi30", "gf85bi30", "hg85bi30")
scen_2050 = c("mc85bi50", "no85bi50", "ac85bi50", "cc85bi50", "gf85bi50", "hg85bi50")
scen_2070 = c("mc85bi70", "no85bi70", "ac85bi70", "cc85bi70", "gf85bi70", "hg85bi70")


## Make a list of SDM columns needed ----
results_columns = c("searchTaxon",        ## From the ALA/ GBIF download code
                    "Family",             ## From Anthony Manea's spreadsheet, will be affected by taxonomy....

                    "Maxent_records",     ## No. records used in the SDM
                    "Aus_records",        ## No. AUS records     :: from the R workflow
                    "AOO",                ## Global Area of occurrence
                    "KOP_count",          ## Number of koppen zones each species is found in...

                    "Number_var",        ## No. maxent variables :: from Maxent code
                    "Var_pcont",         ## Maxent Variable with highest permutation importance
                    "Per_cont",          ## The permutaiton importance of that variable
                    "Var_pimp",          ## Maxent Variable with highest permutation importance
                    "Perm_imp",          ## The permutaiton importance of that variable
                    "Iterations",               ## No. iterations
                    "Training_AUC",             ## training AUC
                    "Max_tss",                  ## Maximium True skill statistic
                    "Number_background_points", ## No. background points
                    "Logistic_threshold",
                    "Omission_rate"             ## Maxent threshold)
)



## List of SDM packages ----
sdmgen_packages <-  c("ff",                   
                      "things",               
                      "raster",               
                      "dismo",                
                      "rJava",               
                      "sp", 
                      "sf",
                      "stars",
                      "latticeExtra",         
                      "data.table",           
                      "devtools",
                      "Hmisc",
                      "roxygen2",            
                      "rgdal",
                      "readxl",
                      "rgeos",  
                      "rgbif",
                      "gdalUtils",            
                      "rmaxent",              
                      "readr",               
                      "plyr",
                      "dplyr",                
                      "tidyr",                
                      "readr",                
                      "rnaturalearth",       
                      "rasterVis",            
                      "RColorBrewer",         
                      "latticeExtra",
                      "parallel",
                      "stringr",              
                      "Taxonstand",
                      "terra",
                      "textclean",
                      "CoordinateCleaner",    
                      "gsubfn",               
                      "PerformanceAnalytics",
                      "utf8",
                      "rvest",                
                      "magrittr",             
                      "devtools",             
                      "ggplot2",             
                      "reshape2",             
                      "rmarkdown",            
                      "flexdashboard",        
                      "shiny",                
                      "ENMeval",              
                      "tibble",               
                      "ncdf4",                
                      "Cairo",                
                      "taxonlookup",         
                      "kgc",                  
                      "betareg",              
                      "gridExtra",            
                      "grid",                 
                      "lattice",             
                      "ConR",                 
                      "writexl",              
                      "sf",                   
                      "ggmap",               
                      "DataCombine",         
                      "exactextractr",        
                      "mgcv",                 
                      "doSNOW",               
                      "tidyverse",            
                      "ggpubr",              
                      "GGally",             
                      "maptools")

HMB3/nenswniche documentation built on Jan. 31, 2023, 11:46 p.m.