The windscape R package models landscape connectivity by wind dispersal. It includes functions to help you download wind data, model wind diffusion times across a region, visualize the results, and test statistical relationships between wind and your ecological data.
Usage
Here’s how to download wind data for a particular place and time (the Gulf of Mexico during Hurricane Katrina, in this case) from the Climate Forecast System Reanalysis, simulate the flow of wind-borne particles, and visualize the results as a map:
library(windscape)
library(tidyverse)
w <- cfsr_dl(years = 2005, months = 8, days = 28, hlim = c(23, 23), # request CFSR data for 28 Aug 2005, 11 pm UTC
xlim = c(-99, -78) + 360, ylim = c(17, 35)) %>% # spatial bounding box for which to get data
shift(dx = -360) %>% # shift longitudes from CFSR's [0:360] range to standard [-180:180] range
wind_field() # format as a wind_field object
f <- particle_flow(w, p = 9000, n_iter = 200, scale = .005, direction = "both") # generate particle trails
md <- map_data("world") # get reference data used to plot land masses
ggplot() + # make map
geom_polygon(data = md, aes(long, lat, group = group), fill = "black") +
geom_path(data = f, aes(x, y, group = g, color = t), linewidth = .25) +
geom_polygon(data = md, aes(long, lat, group = group), fill = "black", color = NA, alpha = .25) +
coord_cartesian(xlim = ext(w)[1:2], ylim = ext(w)[3:4], expand = F) +
scale_color_gradientn(colors = c("gold", "orange", "red", "purple", "blue", "darkblue")) +
theme_void() +
theme(legend.position = "none")
As a second example, here’s how to use a longer-term time series of wind data to calculate estimated wind travel times from a particular location. These wind travel times can be used to make a map as done here, or analyze relationships between wind and ecological patterns as demonstrated in the package vignette:
site <- vect(matrix(c(-105, 40), 1)) # lat-lon coordinates of a focal site
lc <- rast(system.file("extdata/cfsr_usa.tif", package = "windscape")) %>% # load wind time series rasters
wind_series(order = "uuvv") %>% # convert to a formal wind field time series object
wind_rose() %>% # summarize into wind rose conductance object
wind_graph("downwind") %>% # format as connectivity graph
least_cost_surface(site) # calculate least cost path from coordinates
plot(lc, main = "outbound wind travel time (hours)") # plot map of travel times
contour(lc, add = TRUE)
points(site, col = "red") # add origin location to map