RAVeM
# install packages if not installed
if (!requireNamespace("sketchy", quietly = TRUE)) {
install.packages("sketchy")
}
packages <- c("plyr", "dplyr", "tidyverse", "sf", "terra", "data.table")
# install/ load packages
sketchy::load_packages(packages = packages)We can download the amphibian data we extracted from the DASCO v2.5 data (after adding taxonomic information). We also download the geographic data of world mountains. The following code downloads these files to a local folder defined by the user:
# download observations
download.file("https://figshare.com/ndownloader/files/58775401",
destfile = file.path(local_folder, "DASCO_v2.5_Amphibians.csv"),
mode = "wb")
# download mountain polygons
download.file("https://figshare.com/ndownloader/files/58776649",
destfile = file.path(local_folder, "GMBA_Inventory_v2.0_standard_basic.zip"),
mode = "wb")The files can also be downloaded manually from these links:
Now unzip the mountain polygons file into the same local folder.
The following code loads the mountain polygons (selecting specific columns) and the DASCO v2.5 amphibian records:
# Load amphibians DASCO v2.5
dasco_amphibians <- fread(file.path(local_folder, "DASCO_v2.5_Amphibians.csv"))
# Load polygons delimitating mountains (Standard basic layer from Snethlage et al. 2022)
mountain_polygons <- vect(file.path(local_folder , "GMBA_Inventory_v2.0_standard_basic.shp"))
# select specific columns
mountain_polygons <- mountain_polygons[, c(
"MapName",
"GMBA_V2_ID",
"MapUnit",
"Area",
"Perimeter",
"Elev_Low",
"Elev_High",
"Level_01",
"Level_02",
"Level_03",
"Level_04",
"Level_05",
"Level_06",
"Level_07",
"Level_08",
"Level_09",
"Level_10",
"Countries",
"Feature"
)]This section performs a spatial join to extract polygon data for each point, identifying which occurrence records fall within mountain boundaries.
# Convert points and mountain polygons to sf objects
dasco_sf <- st_as_sf(
x = dasco_amphibians,
coords = c("Longitude", "Latitude"),
crs = 4326
)
mountains_sf <- st_as_sf(mountain_polygons)
mountains_sf <- st_make_valid(mountains_sf) # make sure geometries are valid
mountains_sf <- st_simplify(mountains_sf, dTolerance = 0.001) # Simplify complex geometric relationships
# Perform a spatial join to extract polygon data for each point
# Note: Use st_join() to join points and polygons based on their spatial relationship
dasco_mountains <- st_join(dasco_sf, mountains_sf, join = st_intersects)
head(dasco_mountains)| taxon | location | Realm | Database | speciesKey | kingdom | phylum | class | order | family | MapName | GMBA_V2_ID | MapUnit | Area | Perimeter | Elev_Low | Elev_High | Level_01 | Level_02 | Level_03 | Level_04 | Level_05 | Level_06 | Level_07 | Level_08 | Level_09 | Level_10 | Countries | Feature | geometry |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-0.217 5.55) |
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-2.489 10.07917) |
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-2.509667 10.0585) |
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-1.006333 9.491167) |
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-0.984333 9.397833) |
| Afrixalus fulvovittatus | Ghana | terrestrial | GBIF | 2426552 | Animalia | Chordata | Amphibia | Anura | Hyperoliidae | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | POINT (-2.6455 5.26405) |
# Remove records not overlapping with mountains
amphibians_in_mountains <- dasco_mountains %>%
drop_na(MapName)We can also save the extracted records in mountains in a data frame format:
# Convert geometry to separate lon/lat columns
amphibians_in_mountains_df <- amphibians_in_mountains %>%
mutate(
lon = st_coordinates(.)[,1],
lat = st_coordinates(.)[,2]
) %>%
st_drop_geometry() # Remove geometry columnHere we subset the mountain polygons to keep only those mountains with amphibian records:
# Subset the mountain polygon to keep only the mountains with records
mountains_with_records <- unique(amphibians_in_mountains$MapName)
mountains_with_records_shp <-
mountain_polygons[mountain_polygons$MapName %in% mountains_with_records]We can visualize the results by plotting the mountain polygons with records and overlaying the amphibian occurrence points. Let’s first create a world map higlighting the selected mountain ranges:
# Plot world map
maps::map("world",
col = "#A0DFB980",
fill = TRUE,
border = "white",
xlim = c(-180, 180),
ylim = c(-60, 80))
# Add mountain polygons on top
plot(mountains_with_records_shp,
add = TRUE,
col = "#366A9F80",
border = "#366A9F80",
lwd = 1)
Now we can add the amphibian occurrence points on top of the mountain polygons:
points(
amphibians_in_mountains$geometry,
col = adjustcolor("green4", alpha.f = 0.1),
pch = 20,
cex = 0.1
)
Finally we can save the results as shapefiles and a CSV file for further analysis:
# Save as .shp
st_write(
amphibians_in_mountains,
file.path(local_folder, "amphibians_in_mountains.shp"),
append = FALSE
)
# Saving in the Shiny folder as this will be used for visualization
writeVector(
mountains_with_records_shp,
file.path(local_folder, "/Shp_Mt_W_records.shp"),
filetype =
"ESRI Shapefile"
)
# Write to CSV
data.table::fwrite(
amphibians_in_mountains_df,
file.path(local_folder, "amphibians_in_mountains.csv")
)─ Session info ───────────────────────────────────────────────────────────────
setting value
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