Tarawi Nature Reserve

Tools and Libraries

I will be using R with the following selection of libraries in this blog post:

library(rinat)
library(dplyr)
library(lubridate)
library(sf)
library(mapview)
library(leafpop)

• rinat: Access iNaturalist data with R.

• dplyr: A Grammar of Data Manipulation.

I use the library here to manage relative paths of the project:

here::i_am("regions/Kiama-walk.qmd")

here() starts at /Users/z3529065/proyectos/CES/code-4-iNat

I save the downloaded inat data into this data folder at the root of the project folder:

if (!dir.exists(here::here("data")))
    dir.create(here::here("data"))
inat_obs_data <- here::here("data", "inat-obs.rds")

This will query all the observations from my iNaturalist user. I will save this to a data folder:

if (file.exists(inat_obs_data)) {
    user_obs <- readRDS(inat_obs_data)
} else {
    user_obs <- get_inat_obs_user("NeoMapas",maxresults = 5000) |> 
        mutate(dts=date(datetime), year=year(dts), month=month(dts))
    saveRDS(user_obs, inat_obs_data)
}

I can make this object spatially explicit using the function st_to_sf from package sf:

user_obs_xy <- st_as_sf(user_obs,coords=c("longitude","latitude"), crs=4326)

For this post, I am focusing on the observation made on the 9 March 2025:

tarawi_obs <- filter(user_obs_xy, 
  dts %in% c(
    "2024-04-04", "2024-04-05", "2024-04-06",
    "2024-04-07", "2024-04-08", "2024-04-09",
    "2024-04-10"))

This is the number of observations for that day:

nrow(tarawi_obs)

[1] 51

And this is the approximate number of species (or other taxa) included in those observations:

n_distinct(tarawi_obs$species_guess)

[1] 37

A quick map with the default image of each observation can be made with mapview:

mapview(tarawi_obs,
    map.types = c("OpenStreetMap.DE", "Esri.WorldImagery","CyclOSM"),
    layer.name = c("Obs from Tarawi NR"),
    popup =
     popupImage(tarawi_obs$image_url, src = "remote"))

That’s fantastic!