obisindicators

library(obisindicators)
library(dplyr)

Get biological occurrences

Use the 1 million records subsampled from the full OBIS dataset otherwise available at https://obis.org/data/access.

occ <- occ_1M # occ_1M OR occ_SAtlantic

Create a global h3 hexagonal grid

hex_res <- 1  # hex_res 0 is too big to work, all others work
hex <- obisindicators::make_hex_res(hex_res)
# mapview::mapview(hex)  # you can view the hex grid with h3 IDs 

# === Then assign cell numbers to the occurrence data:
occ <- occ %>% 
  mutate(
    cell = h3::geo_to_h3(
      data.frame(decimalLatitude, decimalLongitude),
      res = hex_res))

Calculate indicators

The following function calculates the number of records, species richness, Simpson index, Shannon index, Hurlbert index (n = 50), and Hill numbers for each cell.

Perform the calculation on species level data:

idx <- calc_indicators(occ)

Add cell geometries to the indicators table (idx):

grid <- hex %>% 
  inner_join(
    idx,
    by = c("hexid" = "cell"))
# you could now visualize with:
# plot(grid["es"])
# mapview::mapview(grid["es"])

Plot maps of indicators

Let’s look at the resulting indicators in map form.

# ES(50)
gmap_indicator(grid, "es", label = "ES(50)")

# Shannon index
gmap_indicator(grid, "shannon", label = "Shannon index")

# Number of records, log10 scale, Robinson projection (default)
gmap_indicator(grid, "n", label = "# of records", trans = "log10")

# Number of records, log10 scale, Geographic projection
gmap_indicator(grid, "n", label = "# of records", trans = "log10", crs=4326)