AOU_species_codes.RdA dataframe containing the common names (eg "Scarlet Tanager"), AOU alphabetic code (eg "SCTA") and AOU numeric code (eg. 6080) The common name and alphabetic codes are eay to locate for a given species online. The AOU numeric code is needed because the actual BBS dataframe does not contain species names, just the numeric codes.
AOU_species_codes
A data frame...
Numeric AOU species codes. Typically 4 numbers
4-letter AOCUspecies code
Common name of species
Latin binomial genus and species
code for taxonomic order
Some records from offical list have been removed because they don't have stable names, eg., they are a defined species. Note that the row number (aka line number) DOES NOT equal the AOU code. When access these data be sure to be clear whether you are using the line number or using the actual code. See below for examples.
## Example 1: Accessing basic information ## Determine row number by species common name ### The Scarlet Tanager is in row 897 and has the AOU specied ### number (aka spp code) of 6080 row.number.i <- which(AOU_species_codes$name == "Scarlet Tanager") ## Determine row number by Aou alphabetic code row.number.i <- which(AOU_species_codes$alpha.code == "SCTA") ## Access code by row / line number ### These are equivalent: AOU_species_codes[row.number.i,]#> spp.num alpha.code name spp order #> 897 6080 SCTA Scarlet Tanager Piranga olivacea 1057AOU_species_codes[812,]#> spp.num alpha.code name spp order #> 897 6080 SCTA Scarlet Tanager Piranga olivacea 1057#Load data on AOU species codes ## Example 2: Seting up data for analysis of ### the relationship between abundance and landcover ### Focal species: European starling "EUST" ### Which ROW NUMBER in the list of species codes #### does the European startling occur in? EUST.row.number <- which(AOU_species_codes$alpha.code == "EUST") ### Look at just the EUST row of the #### AOU_species_codes dataframe AOU_species_codes[EUST.row.number,]#> spp.num alpha.code name spp order #> 717 4930 EUST European Starling Sturnus vulgaris 864#### The numeric code for EUST is 494) #### save it to an object EUST.numeric.code <- 4940 ### look at names of the columns in the BBS_PA data set with ### the data on bird abundance names(BBS_PA)#> [1] "countrynum" "statenum" "Route" "RPID" "Year" #> [6] "Aou" "StopTotal" "SpeciesTotal"#### The column with the Aou species codes is called "Aou" #### load dplyr package #library(dplyr) #### Subset just the rows of data for the European startling #### That is, the rows where the Aou code = 4940 #BBS_PA_EUST <- BBS_PA %>% filter(Aou == 4940) #### See how the dataframe has change #dim(BBS_PA) #dim(BBS_PA_EUST) #summary(BBS_PA_EUST) ## Save output ### data can be saved with write.csv(), eg: ### write.csv(BBS_PA_EUST, file = "starling_abundance.csv") #### If you are studying how the abundance of a species changes #### as landcover changes you can now merge this subset of the data #### with the GIS landcover data.