README.md

In patrickbarks/bbs: North American Breeding Bird Survey (BBS) in R

rBBS

An R package to work with data from the North American Breeding Bird Survey (BBS).

The BBS is a large-scale bird monitoring program that was initiated in 1966 and currently encompasses over 4,600 active survey routes in North America. Each survey involves 50 three-minute point counts conducted at half-mile intervals along a 24.5-mile route by a skilled volunteer.

The BBS is a cooperative effort between the United States Geological Survey (USGS) and Environment and Climate Change Canada's Canadian Wildlife Service (CWS). See the terms of use for BBS data here.

BBS datasets

The BBS provides two main datasets:

50-stop

• includes bird counts for each of the 50 stops on a given survey
• only consistently available from 1997 onward

10-stop

• bird counts binned into groups of 10 stops (i.e. stops 1-10, 11-20, ..., 41-50)
• consistently available for all survey years

Installation

Install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("patrickbarks/rBBS")

Usage

Download data

To work with BBS data we first need to download it from the USGS ftp server. This can be done manually following the links at https://www.pwrc.usgs.gov/bbs/, or by using the function bbs_download.

# "." gives the working directory; you may prefer to specify a different one
bbs_download(dest = ".")

# or, download a specific geographic subset
bbs_download(dest = ".", states = c("Washington", "Oregon", "California"))

The default options download metadata files and 10-stop data. The 50-stop data can be downloaded by adding the argument fifty_stop = TRUE.

Build metadata tables

Metadata tables can be built using the bbs_meta_* functions.

bcr <- bbs_meta_bcr(bbs_dir = ".")
strata <- bbs_meta_strata(bbs_dir = ".")
regions <- bbs_meta_regions(bbs_dir = ".")
routes <- bbs_meta_routes(bbs_dir = ".")
weather <- bbs_meta_weather(bbs_dir = ".")
species <- bbs_meta_species(bbs_dir = ".")

Build bird count tables

Tables with bird-count data can be built using the bbs_build_* functions:

bbs_10 <- bbs_build_10(bbs_dir = ".") # 10-stop data
bbs_50 <- bbs_build_50(bbs_dir = ".") # 50-stop data

Build in counts of zero

The original BBS data does not include counts of zero (i.e. instances where a species was not observed on a given route), but these can be built in by setting zeros = TRUE in the bbs_build_* functions.

Note that the full dataset with zeros includes more than 92 million rows, which requires some time to build and a few gigabytes of memory. It's therefore a good idea to subset to the species, years, or locations of interest within the call to bbs_build_*, e.g.

bbs_recent <- bbs_build_10(bbs_dir = ".", zeros = TRUE, years = 2010:2015)

Joining BBS tables

Column names and types are standardized across tables to make joining easy.

# join count data with route-specific data, including geographic coordinates
bbs_recent_route <- merge(bbs_recent, routes)

Worked example

Let's say we're interested in the distribution of the Western Meadowlark within the contiguous United States. We can find its American Ornithological Union species code (aou) in the metadata table species.

species$aou[species$english_common_name == 'Western Meadowlark']
#> [1] 5011

We'll use the aou code to build a table with count data for the Western Meadowlark (including counts of zero), limited to the United States.

wm <- bbs_build_10(".", zeros = TRUE, countries = 'United States', aou = 5011)

Next, we'll subset out Alaska to keep things contiguous (sorry Alaska).

ak_num <- regions$state_num[regions$state_name == 'Alaska']
wm <- wm[wm$state_num != ak_num,]

Next, we'll summarize the occurrence of the Western Meadowlark by route (unique combinations of country_num x state_num x route). We'll calculate occurrence simply as any instance of species_total > 0 on a given route.

wm_occur <- aggregate(
  list(occur = wm$species_total),
  list(country_num = wm$country_num, state_num = wm$state_num, route = wm$route),
  FUN = function(x) ifelse(any(x > 0), TRUE, FALSE)
)

Finally, we'll join the routes table to get geographic coordinates.

wm_occur <- merge(wm_occur, routes)

And then we're ready to plot!

library(ggplot2)
states <- map_data("state")

ggplot(states) + 
  geom_polygon(aes(long, lat, group = group), fill = 'grey70', col = 'white') +
  geom_point(data = wm_occur, aes(longitude, latitude, col = occur), size = 2) +
  scale_color_brewer(palette = 'Set1', name = 'Observed')

Contributions

All contributions are welcome. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

patrickbarks/bbs documentation built on May 28, 2019, 12:34 a.m.