American Ornithological Union (AOU) species codes for North-American birds

A 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

Format

A data frame...

spp.num

Numeric AOU species codes. Typically 4 numbers

alpha.code

4-letter AOCUspecies code

name

Common name of species

spp

Latin binomial genus and species

order

code for taxonomic order

Details

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.

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  1057
AOU_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.