In nicholasjclark/BBS.occurrences: BBS community composition and co-occurrence analysis

knitr::opts_chunk$set(echo = TRUE)
library(knitr)
opts_chunk$set(tidy.opts=list(width.cutoff=50),tidy=TRUE)

This appendix describes how to extract flyway and county grouping variables for each BBS survey site. These variables can be accessed in the BBS.occurrences library using data('Site.descriptors').

Load the cleaned BBS survey dataset that was prepared in Appendix S1

load("./BBS.Data.cleaned/BBS.2003-2009.filtered.Rdata")

Load the Landcover summary data for survey points that was prepared in Appendix S2

uniq.points.landcover <- read.csv("./LandCover/MODIS_Landcover_summary.csv")

Put landcover covariates and site descriptors into a single dataframe

library(dplyr)
Site.descriptors = sites.df %>%
  dplyr::left_join(uniq.points.landcover, 
                   by = c('Latitude' = 'lat',
                          'Longitude' = 'long',
                          'year' = 'year')) %>%
  dplyr::select(-modis.band, -richness, -simpsons.d, -simpsons.evenness,
                -no.data.fill)

Here we write a function to group sites into counties. This is accomplished by loading the US Government Census Cartographic Boundaries shapefile. Note, this file can be manually downloaded here: https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html if the tempfile strategy below fails. We then use a simple function to ensure that the coordinate system of our latitudes and longitudes match that of the shapefile and then intersect each unique point with the counties

# install.packages('sf')
library(sf)
library(dplyr)
temp <- tempfile()
download.file('http://www2.census.gov/geo/tiger/GENZ2017/shp/cb_2017_us_county_5m.zip', temp)
county.shp <- sf::st_read(unzip(temp, 'cb_2017_us_county_5m.shp'))
unlink(temp)

geo_inside <- function(lon, lat, map, variable) {
#Escape the variable name
  variable <- dplyr::enquo(variable)

#Convert each unique point into an sf object
  pt <- tibble::data_frame(x = lon,
                           y = lat) %>%
    sf::st_as_sf(coords = c("x", "y"), 
                 crs = 4269) %>%
    sf::st_transform(crs = sf::st_crs(county.shp))

#Intersect the point with the map object
pt %>% 
  sf::st_join(map) %>% 
  dplyr::pull(!!variable)
}

\pagebreak

Next we write a similar function to group sites into regions based on recognised North American migratory flyways. This is accomplished by loading the US Fish and Wildlife Service's WaterfowlFlyways shapefile. Note, this file can be manually downloaded here: https://ecos.fws.gov/ServCat/Reference/Profile/42276 if the tempfile strategy below fails. We then use a simple function to ensure that the coordinate system of our latitudes and longitudes match that of the shapefile and then intersect each unique point with the flyway layer

# install.packages('sf')
library(sf)
library(dplyr)
temp <- tempfile()
download.file('https://ecos.fws.gov/ServCat/DownloadFile/113670', temp)
fly.shp <- sf::st_read(unzip(temp, 'WaterfowlFlyways.shp'))
unlink(temp)

geo_inside <- function(lon, lat, map, variable) {
#Escape the variable name
  variable <- dplyr::enquo(variable)

#Convert each unique point into an sf object
  pt <- tibble::data_frame(x = lon,
                           y = lat) %>%
    sf::st_as_sf(coords = c("x", "y"), 
                 crs = 4269) %>%
    sf::st_transform(crs = sf::st_crs(fly.shp))

#Intersect the point with the map object
pt %>% 
  sf::st_join(map) %>% 
  dplyr::pull(!!variable)
}

Now that all of our site-level covariates are stored together, we can intersect each unique point with the shapefile above to group the sites into counties.

point_counties = Site.descriptors %>%
  dplyr::select(Latitude, Longitude) %>%
  dplyr::distinct() %>%
  dplyr::mutate(County = geo_inside(lon = Longitude, 
                                         lat = Latitude,
                                  map = county.shp,
                                  variable = NAME))

Site.descriptors = Site.descriptors %>%
  left_join(point_counties, by = c('Latitude' = 'lat',
                                      'Longitude' = 'lon'))

Repeat for flyways and then join the grouping information back to the descriptors data

point_site.groups = Site.descriptors %>%
  dplyr::select(lat, lon) %>%
  dplyr::distinct() %>%
  dplyr::mutate(site.group = geo_inside(lon = lon, 
                                         lat = lat,
                                  map = fly.shp,
                                  variable = NAME))

Site.descriptors = Site.descriptors %>%
  left_join(point_site.groups, by = c('Latitude' = 'lat',
                                      'Longitude' = 'lon'))
colnames(Site.descriptors) <- stringr::str_to_title(colnames(Site.descriptors))

Save the grouped descriptor data for downstream analysis. Note, the flyway variables can be directly accessed in the BBS.occurrences library using data('Site.descriptors').

dir.create('./Analysis_data')
save(Site.descriptors, file = './Analysis_data/Site.descriptors.Rdata')

nicholasjclark/BBS.occurrences documentation built on July 19, 2020, 8:31 p.m.