



The most recent map products from the model are shown below, in addition to an interactive map of the latest prediction.
Status of environmental products from CMEMS:
This example shows both model predictions and environmental covariates used to estimate the models. Change the date and selected variable to explore these relationships in more detail.
Clone the repository: https://github.com/joshcullen/CEG_operationalization
library(curl)
library(httr)
library(tidyverse)
library(glue)
= function(save_dir, file_type, start_date, end_date){
download_files_git
<- switch(file_type,
folder "raster" = "rasters",
"image" = "img")
# Find files from repo folder
<- httr::GET(glue("https://api.github.com/repos/joshcullen/CEG_operationalization/contents/model_prediction/TopPredatorWatch/{folder}"))
prods ::stop_for_status(prods) #check to make sure no errors w/ request (should return nothing to console if working properly)
httr<- unlist(lapply(content(prods), "[", "name"), use.names = F)
filelist <- data.frame(files = filelist,
prod_df date = gsub("^[A-Za-z]+\\_|\\.tiff$", "", filelist) |>
as.Date()
)
# Filter files by date range
<- prod_df |>
prod_filt filter(date >= as.Date(start_date),
<= as.Date(end_date)) |>
date pull(files)
<- glue("https://raw.githubusercontent.com/joshcullen/CEG_operationalization/main/model_prediction/TopPredatorWatch/{folder}/{prod_filt}")
git_url <- glue("{save_dir}/{prod_filt}")
file_dest
::multi_download(urls = git_url, destfiles = file_dest)
curl
}
# Download files
download_files_git(save_dir = "~/Downloads",
file_type = "raster",
start_date = "2025-06-25",
end_date = "2025-06-30")
download_files_git(save_dir = "~/Downloads",
file_type = "image",
start_date = "2025-06-25",
end_date = "2025-06-30")