Whooping crane stopover habitat

• Overview of whooping cranes
  • 
  • One of the first species listed under the endangered species act.
  • Endangered species act requires critical habitat to be designated soon after listing
    • Critical habitat designated in 1978 (link)
• Data
  • Opportunistic observations of Whooping cranes in Nebraska from 1988-2012
  • Data is available from Data Dryad.
  • Lets take a look at the data using the code below

  library(rgdal)
  library(utils)
  library(raster)
  library(latticeExtra)
  library(rgeos)
  library(sp)
  library(rgdal)
  library(RColorBrewer)
  
  url <- "https://www.dropbox.com/s/2c54py29spx9yir/whooping%20crane%20data%20with%20all%20covariates.csv?dl=1"
  data <- read.csv(url)
  colnames(data)[54:55] <- rev(colnames(data)[54:55])
  
  tmpdir <- tempdir()
  url <- "https://www.dropbox.com/s/kl7rfqn05m92djh/Nebraska.zip?dl=1"
  file <- basename(url)
  download.file(url, file)
  unzip(file, exdir = tmpdir)
  shapefile <- paste(tmpdir, "/Nebraska/Nebraska.shp", sep = "")
  state <- readOGR(shapefile, layer = "Nebraska")

  ## OGR data source with driver: ESRI Shapefile 
  ## Source: "/private/var/folders/2x/rc9k5fxn2gx_gr790742wl3h0000gn/T/RtmpLaeQ5t/Nebraska/Nebraska.shp", layer: "Nebraska"
  ## with 1 features
  ## It has 8 fields

  data.whooper <- data
  data.whooper <- data.whooper[which(data.whooper$y == 1), ]
  data.whooper <- data.whooper[rev(order(data.whooper$group.size)), ]
  # head(data.whooper)
  
  points = data.frame(x = data.whooper$lat, y = data.whooper$lon, z = data.whooper$group.size)
  coordinates(points) = ~x + y
  proj4string(points) <- CRS(projection(state))
  
  breaks = c(0, 1, 2, 3, 4, 5, 10, 21)
  classes = cut(data.whooper$group.size, breaks)
  meds = tapply(data.whooper$group.size, classes, FUN = median)
  sizes <- (meds/max(meds))^0.57 * 1.5
  
  breaks = c(0, 1, 2, 3, 4, 5, 10, 21)
  classes = cut(data.whooper$group.size, breaks)
  meds = tapply(data.whooper$group.size, classes, FUN = median)
  sizes <- (meds/max(meds))^0.57 * 1.5
  
  l1 = list("SpatialPolygonsRescale", layout.scale.bar(), offset = c(-104, 40), 
      scale = 100/84.44, fill = c("transparent", "black"))
  l2 = list("sp.text", c(-104, 40.25), "0")
  l3 = list("sp.text", c(-102.7, 40.25), "100 km")
  
  plot = spplot(points[, 1], colorkey = FALSE, cex = sizes, col.regions = bpy.colors(n = 100, 
      cutoff.tails = 0.5, alpha = 0.7), scales = list(draw = T), xlim = c(-104.3, 
      -95), ylim = c(39.75, 43.25), cuts = breaks, key.space = "right", alpha = 0.9, 
      legendEntries = c(1, 2, 3, 4, 5, "6-10", "11-21"), auto.key = list(cex = c(0.7), 
          title = "Group size"), sp.layout = list(l1, l2, l3)) + layer(sp.polygons(state))
  print(plot)

  

• What research questions related to whooping cranes could be answered using these data?
  • Group discussion