Make SpatialGridDataFrame from occurrence data and climate raster match each other

Question

I've two SpatialGridDataFrame, one created from species occurrence data, the other from a raster of climate data. I need to make the two grids match each other to perform a species diversity model. I'm struggling to get the grid topology of the two objects identical, which is required to run a function I will work with.

Here're subsets of my data for reproducibility:

bioclim <- structure(list(x = c(-83.5543059413, -83.545972608, -83.5376392747, 
-83.5293059414, -83.5209726081, -83.5126392748, -83.5043059415, 
-83.4959726082, -83.4876392749, -83.4793059416, -83.4709726083, 
-83.462639275, -83.4543059417, -83.4459726084, -83.4376392751, 
-83.4293059418, -83.4209726085, -83.4126392752, -83.5543059413, 
-83.545972608, -83.5376392747, -83.5293059414, -83.5209726081, 
-83.5126392748, -83.5043059415, -83.4959726082, -83.4876392749, 
-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417, 
-83.4459726084, -83.4376392751, -83.4293059418, -83.4209726085, 
-83.4126392752, -83.4043059419, -83.5543059413, -83.545972608, 
-83.5376392747, -83.5293059414, -83.5209726081, -83.5126392748, 
-83.5043059415, -83.4959726082, -83.4876392749, -83.4793059416, 
-83.4709726083, -83.462639275, -83.4543059417, -83.4459726084, 
-83.4376392751, -83.4293059418, -83.4209726085, -83.4126392752, 
-83.4043059419, -83.3959726086, -83.3876392753, -83.5543059413, 
-83.545972608, -83.5376392747, -83.5293059414, -83.5209726081, 
-83.5126392748, -83.5043059415, -83.4959726082, -83.4876392749, 
-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417, 
-83.4459726084, -83.4376392751, -83.4293059418, -83.4209726085, 
-83.4126392752, -83.4043059419, -83.3959726086, -83.3876392753, 
-83.379305942, -83.5543059413, -83.545972608, -83.5376392747, 
-83.5293059414, -83.5209726081, -83.5126392748, -83.5043059415, 
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-83.462639275, -83.4543059417, -83.4459726084, -83.4376392751, 
-83.4293059418, -83.4209726085, -83.4126392752, -83.4043059419, 
-83.3959726086, -83.3876392753, -83.379305942, -83.3709726087, 
-83.5543059413, -83.545972608, -83.5376392747, -83.5293059414, 
-83.5209726081, -83.5126392748, -83.5043059415, -83.4959726082, 
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-83.4543059417, -83.4459726084, -83.4376392751, -83.4293059418, 
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-83.4293059418, -83.4209726085, -83.4126392752, -83.4043059419, 
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-83.379305942, -83.3709726087, -83.5543059413, -83.545972608, 
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-83.5543059413, -83.545972608, -83.5376392747, -83.5293059414, 
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-83.3876392753, -83.379305942, -83.3709726087, -83.5543059413, 
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-83.379305942, -83.3709726087, -83.5543059413, -83.545972608, 
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-83.4793059416, -83.4709726083, -83.462639275, -83.4543059417, 
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-83.4043059419, -83.3959726086, -83.3876392753, -83.379305942, 
-83.3709726087, -83.5543059413, -83.545972608, -83.5376392747, 
-83.5293059414, -83.5209726081, -83.5126392748, -83.5043059415, 
-83.4959726082, -83.4876392749, -83.4793059416, -83.4709726083, 
-83.462639275, -83.4543059417, -83.4459726084, -83.4376392751, 
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-83.4209726085, -83.4126392752, -83.4043059419, -83.3959726086, 
-83.3876392753, -83.379305942, -83.3709726087), y = c(8.61236071670001, 
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8.43736071740001), CHELSA_bio1_1981.2010_V.2.1 = c(24.55, 24.55, 
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25.65)), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
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505L, 506L), class = "data.frame")

occ_df <- structure(list(lon = c(-83.41, -83.51, -83.41143, -83.525, -83.46, 
-83.46, -83.43, -83.49, -83.41, -83.44, -83.46944, -83.427778, 
-83.516667, -83.433609, -83.411431, -83.55, -83.516805), lat = c(8.54, 
8.56, 8.540221, 8.540278, 8.51, 8.52, 8.52, 8.45, 8.52, 8.52, 
8.51805, 8.525, 8.566667, 8.531329, 8.540221, 8.45, 8.569294), 
    `Chrysochlamys glauca` = c(1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 
    0, 1, 1, 1, 1, 1, 0), `Ocotea atirrensis` = c(0, 1, 0, 0, 
    1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -17L))

Here is what I currently have

library(dplyr)

clim <- terra::rast(bioclim)
terra::crs(clim) <- "+proj=longlat +datum=WGS84 +no_defs"

clim_sg_df <- raster::raster(clim) %>% 
  as("SpatialGridDataFrame")


occ <- occ_df %>% 
  # rounding lon/lat to fix inconstant coordinate intervals when using gridded()
  mutate(across(
    matches("lon|lat"),
    ~ round(.x, 2) 
  ))

sp::coordinates(occ) <- ~ lon + lat
sp::proj4string(occ) <- "+proj=longlat +datum=WGS84 +no_defs"
sp::gridded(occ) <- TRUE
occ_sg_df <- as(occ_sp, "SpatialGridDataFrame")

# terra::plot(clim_sg_df)
# terra::plot(occ_sg_df, add = TRUE)

I have two major issues. First, I cannot plot the two grids on top of each other despite having the same projection, so I suspect an error in my code. Second, grid cells between the two objects are of different sizes and I really don't know how to homogenise them.

I will need to have both clim_sg_df and occ_sg_df match each other so that the following condition returns TRUE:

identical(as(occ_sg_df, "SpatialGrid"), as(clim_sg_df, "SpatialGrid"))

Note that I need to keep all cells from the raster and use the sp package because that's what the functions I will use for the analyses require.

Answer 1

Instead of gridded you should use rasterize. For example with this raster and these points:

library(terra)
r <- rast(system.file("ex/elev.tif", package="terra"))
set.seed(123)
pts <- spatSample(r, 1000, xy=TRUE, na.rm=TRUE, replace=TRUE)
xy <- as.matrix(pts[, c("x", "y")])

You can do

spp <- rasterize(xy, r)

or count the number of occurrences by cell

spp <- rasterize(xy, r, 1, fun=sum)

Instead of a matrix, you can also use a SpatVector of points.

It is never easy to plot two overlapping rasters on one map but Grzegorz Sapijaszko shows how to do it. You can also use transparency. But it generally makes more sense to just add the points on top of the raster:

plot(r)
points(xy, cex=.5)

Or the centers of the cells that have a point

pspp <- as.points(spp)
plot(r)
points(pspp, cex=.25)

Or add the outlines of the cells (if the number of cells with an occurrence is not too high)

polspp <- as.polygons(spp)
plot(r)
lines(polspp)

It is more convenient to use example data that ship with R, but with your example data you can do

library(terra)
r <- rast(bioclim)
pts <- vect(data.frame(occ_df), geom=c("lon", "lat"))
x <- rasterize(pts, r)
plot(r)
lines(as.polygons(x))
points(pts, col="red")

In your example data you have multiple species.

pts
# class       : SpatVector 
# geometry    : points 
# dimensions  : 17, 2  (geometries, attributes)
# extent      : -83.55, -83.41, 8.45, 8.569294  (xmin, xmax, ymin, ymax)
# coord. ref. :  
# names       : Chrysochlamys.glauca Ocotea.atirrensis
# type        :                <num>             <num>
# values      :                    1                 0
#                                  1                 1
#                                  1                 0

You could subset pts to a single species like this

glauca <- pts[pts$Chrysochlamys.glauca == 1, ]

You can also use raster::rasterize (with a RasterLayer and a matrix or a SpatialPointsDataFrame) if you prefer. Or coerce the objects from terra back to raster/sp like this:

library(raster)
xr <- raster(x)
pp <- as(pts, "Spatial")
plot(xr)
points(pp)