RAVeM
Adrian García-Rodríguez, David Umhauer & Marcelo Araya-Salas
October 27, 2025
First we load the required packages:
Warning: package 'terra' was built under R version 4.4.3Warning: package 'data.table' was built under R version 4.4.3Warning: package 'sf' was built under R version 4.4.3This module subsets IUCN species range maps to retain only those species with mountain occurrence records. We will work on the amphibian occurrences data from previous tutorials as an example.
We read the amphibian occurrences data to extract the species names:
# Load occurrences to extract spp names
# Read only needed columns (Here we only need the species names)
amphibians_in_mountains_df <- fread(file.path(local_folder, "amphibians_in_mountains.csv"),
select = c("taxon"))
# Keep only valid names Remove NA values (total 82 spp)
spp.names <- unique(na.omit(amphibians_in_mountains_df$taxon)) Here we load the amphibian distribution maps from IUCN. Note that amphibian maps are split into two shapefiles, so we will need to process both files and then combine them:
amphs1 <- vect(paste0("../INPUTS/IUCNpolygsRAW/AMPHIBIANS_PART1.shp"))
amphs2 <- vect(paste0("../INPUTS/IUCNpolygsRAW/AMPHIBIANS_PART1.shp"))
filtered_amphs1 <- amphs1[amphs1$sci_name %in% spp.names, ]
filtered_amphs2 <- amphs2[amphs2$sci_name %in% spp.names, ]
filtered_amphs <- list(filtered_amphs1, filtered_amphs2)We now keep only the species with mountain occurrence records and homogenize the attribute tables in order to combine them:
# Identify all unique column names across all SpatVectors
all_colnames <- unique(unlist(lapply(filtered_amphs, names)))
# Find intersection of column names
common_cols <- Reduce(intersect, lapply(filtered_amphs, names))
# Subset all vectors to common columns and combine
filtered_amphs <- do.call(rbind, lapply(filtered_amphs, function(v) v[, common_cols]))
spp_polygs <- unique(filtered_amphs$sci_name)The results can be saved as shapefiles for further analysis. Here we save the amphibian polygons:
This section calculates the minimum distance from each occurrence record to the species’ native range polygon. First we need to load the function ‘distance_to_native’ to calculate distances. The function is hosted at the figshare repository of this project:
Now we can estimate the distance to native ranges for all species in the dataset. This process can take some time depending on the number of species and records:
# Adjust object names for analysis
occs.df <- Dasco.occs2
sp.names <- unique(occs.df$taxon)
dist_to_native_list <- pblapply(1:length(sp.names), distance_to_native)
# Remove NULL entries from failed processing
dist_to_native_list <- dist_to_native_list[!sapply(dist_to_native_list, is.null)]
# Combine successful results
dist_to_native_df <- do.call(rbind, dist_to_native_list)The output data frame ‘dist_to_native_df’ contains the original occurrence data along with a new column ‘min_distance_nat_range’ indicating the minimum distance (in kilometers) from each record to the nearest native range polygon.
Users can now set a distance threshold to determine which records to consider as alien. For example, to consider records beyond 50 km from the native range as alien:
García-Rodríguez, A., Lenzner, B., Velasco, J.A. et al. The global distribution patterns of alien vertebrate richness in mountains. Nat Commun 16, 1977 (2025). https://doi.org/10.1038/s41467-025-57214-w
Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", :
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──────────────────────────────────────────────────────────────────────────────---
title: Compute distance to native ranges
subtitle: RAVeM
author: Adrian García-Rodríguez, David Umhauer & Marcelo Araya-Salas
date: "`r Sys.Date()`"
toc: true
toc-depth: 3
toc-location: left
number-sections: true
highlight-style: pygments
format:
html:
df-print: kable
code-fold: show
code-tools: true
css: qmd.css
editor_options:
chunk_output_type: console
---
```{r set root directory}
#| eval: true
#| echo: false
# install knitr package if not installed
if (!requireNamespace("knitr", quietly = TRUE)) {
install.packages("knitr")
}
# set working directory
knitr::opts_knit$set(root.dir = "..")
```
```{r setup style}
#| message: false
#| warning: false
#| echo: false
# options to customize chunk outputs
knitr::opts_chunk$set(
# tidy.opts = list(width.cutoff = 65),
# tidy = TRUE,
message = FALSE
)
local_folder <- "./data/processed"
```
<!-- skyblue box -->
::: {.alert .alert-info}
# Purpose {.unnumbered .unlisted}
- Compute distance to native ranges for species occurrences in mountains
:::
First we load the required packages:
```{r load packages}
#| eval: true
# install packages if not installed
if (!requireNamespace("sketchy", quietly = TRUE)) {
install.packages("sketchy")
}
packages <- c("terra", "data.table", "doParallel", "sf", "sp", "dplyr", "rgdal")
# install/ load packages
sketchy::load_packages(packages = packages)
```
# Get native ranges
This module subsets IUCN species range maps to retain only those species with mountain occurrence records. We will work on the amphibian occurrences data from previous tutorials as an example.
# Load data
We read the amphibian occurrences data to extract the species names:
```{r load-data}
# Load occurrences to extract spp names
# Read only needed columns (Here we only need the species names)
amphibians_in_mountains_df <- fread(file.path(local_folder, "amphibians_in_mountains.csv"),
select = c("taxon"))
# Keep only valid names Remove NA values (total 82 spp)
spp.names <- unique(na.omit(amphibians_in_mountains_df$taxon))
```
# Load IUCN species distribution maps
Here we load the amphibian distribution maps from IUCN. Note that amphibian maps are split into two shapefiles, so we will need to process both files and then combine them:
```{r process-multipart-files}
#| eval: false
amphs1 <- vect(paste0("../INPUTS/IUCNpolygsRAW/AMPHIBIANS_PART1.shp"))
amphs2 <- vect(paste0("../INPUTS/IUCNpolygsRAW/AMPHIBIANS_PART1.shp"))
filtered_amphs1 <- amphs1[amphs1$sci_name %in% spp.names, ]
filtered_amphs2 <- amphs2[amphs2$sci_name %in% spp.names, ]
filtered_amphs <- list(filtered_amphs1, filtered_amphs2)
```
We now keep only the species with mountain occurrence records and homogenize the attribute tables in order to combine them:
```{r process-multipart-files 2}
#| eval: false
# Identify all unique column names across all SpatVectors
all_colnames <- unique(unlist(lapply(filtered_amphs, names)))
# Find intersection of column names
common_cols <- Reduce(intersect, lapply(filtered_amphs, names))
# Subset all vectors to common columns and combine
filtered_amphs <- do.call(rbind, lapply(filtered_amphs, function(v) v[, common_cols]))
spp_polygs <- unique(filtered_amphs$sci_name)
```
## Save results
The results can be saved as shapefiles for further analysis. Here we save the amphibian polygons:
```{r save-results}
#| eval: false
# Save as shapefile (creates 3 files: .shp, .shx, .dbf)
writeVector(filtered_amphs, "../INPUTS/amphibian_distribution_polygons.shp",
filetype = "ESRI Shapefile", overwrite=TRUE)
save.image("3_SubsetIUCNpolygs.RData")
```
<!-- add packages used, system details and versions -->
# Estimate distance to native ranges
This section calculates the minimum distance from each occurrence record to the species' native range polygon. First we need to load the function 'distance_to_native' to calculate distances. The function is hosted at the [figshare repository of this project](https://figshare.com/s/ca1f543aa0b13b07d22a):
```{r}
#| eval: false
source("https://figshare.com/ndownloader/files/58805263")
```
Now we can estimate the distance to native ranges for all species in the dataset. This process can take some time depending on the number of species and records:
```{r prepare-data}
#| eval: false
# Adjust object names for analysis
occs.df <- Dasco.occs2
sp.names <- unique(occs.df$taxon)
dist_to_native_list <- pblapply(1:length(sp.names), distance_to_native)
# Remove NULL entries from failed processing
dist_to_native_list <- dist_to_native_list[!sapply(dist_to_native_list, is.null)]
# Combine successful results
dist_to_native_df <- do.call(rbind, dist_to_native_list)
```
The output data frame 'dist_to_native_df' contains the original occurrence data along with a new column 'min_distance_nat_range' indicating the minimum distance (in kilometers) from each record to the nearest native range polygon.
## Extract alien records by distance thresholds
Users can now set a distance threshold to determine which records to consider as alien. For example, to consider records beyond 50 km from the native range as alien:
```{r}
#| eval: false
# Alien records beyond 50 km from native range
dist_to_native_df$status <- ifelse(dist_to_native_df$min_distance_nat_range <= 50,
"native",
"non-native")
```
## Reference {.unnumbered .unlisted}
García-Rodríguez, A., Lenzner, B., Velasco, J.A. et al. The global distribution patterns of alien vertebrate richness in mountains. Nat Commun 16, 1977 (2025). https://doi.org/10.1038/s41467-025-57214-w
## Session information {.unnumbered .unlisted}
<details>
<summary>Click to see</summary>
```{r session info}
#| echo: false
# if devtools is installed use devtools::session_info()
if (requireNamespace("devtools", quietly = TRUE)) {
devtools::session_info()
} else {
sessionInfo()
}
```
</details>