Visualizing Biodiversity Occurrence Data with R Shiny

As an ecologist, being able to easily visualize biodiversity occurrence data is an essential need as this kind of data visualization provides critical insights into species distribution patterns and ecological requirements, which is essential for understanding biodiversity dynamics in space and time. Moreover,  for pragmatic reasons, fast and simple biodiversity data visualization can help us to define sampling and monitoring strategies in the field, optimizing resource and time allocation. As someone who is a passionate enthusiast for wildlife and nature, being able to visualize species occurrence is also particularly important when I am planning a trip to a new place I have never been, or just “virtually exploring” a far and unknown corner of the planet. 

In this post, I will show a simple way to create a R Shiny app to visualize biodiversity occurrence based on data from the Global Biodiversity Information Facility, aka GBIF, which “is an international network and data infrastructure funded by the world’s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth“. Global Biodiversity Information Facility aggregates data from various scientific sources, and as of today, it contains over 2 billion records of species occurrence from all over the world.

This simple R Shiny app allows users to explore species distributions by selecting a taxonomic group and defining a geographic area of interest using latitude and longitude coordinates to define a polygon (only min and max latitude and longitude are used to define the polygon, thus complex boundaries can not be included here), which will be used to retrieve species occurrence data from GBIF within this quadrilateral. The data will be displayed on an interactive map using the R package leaflet.

In the source code, you can also change  the taxa of interest to be shown in the user interface and the months of the year in which the data was collected, which might be useful for seasonal species.

The user interface should look like this:

Here is the R code!

# Install packages
install.packages(c("shiny", "rgbif", "leaflet", dependencies=T))

# Load packages
require(shiny)
require(rgbif)
require(leaflet)

# Define function to search for occurrences of  specified clades within a polygon (i.e, bounding box=bbox)
search_occurrences <- function(bbox, clade) {
  occ_search_result <- occ_search(
    geometry = paste("POLYGON((", bbox["min_longitude"], " ", bbox["min_latitude"], ",",
                     bbox["min_longitude"], " ", bbox["max_latitude"], ",",
                     bbox["max_longitude"], " ", bbox["max_latitude"], ",",
                     bbox["max_longitude"], " ", bbox["min_latitude"], ",",
                     bbox["min_longitude"], " ", bbox["min_latitude"], "))"),
    month = 1, 12,###define months of the year
    scientificName = clade,
    hasCoordinate = TRUE
  )
  return(occ_search_result)
}

# Define user interface
ui <- fluidPage(
  titlePanel("Species Occurrence"),
  sidebarLayout(
    sidebarPanel(
      selectInput("clade", "Choose a clade:",
                  choices = c("Aves", "Coleoptera", "Amphibia", "Plantae", "Mammalia", "Actinopterygii", "Insecta"),#you can change the default clades according to your taste in biodiversity
                  selected = "Aves"), #first clade to be shown in the drop down box
      numericInput("min_longitude", "Minimum Longitude:", value = -9),##by default you will have the approximate borders of portugal, but this can be changed in the user interface or directly here
      numericInput("max_longitude", "Maximum Longitude:", value = -6),
      numericInput("min_latitude", "Minimum Latitude:", value = 36),
      numericInput("max_latitude", "Maximum Latitude:", value = 42)
    ),
    mainPanel(
      leafletOutput("map")
    )
  )
)

# Define server logic
server <- function(input, output) {
  # Render the leaflet map based on user's clade selection and polygon coordinates
  output$map <- renderLeaflet({
    clade <- input$clade
    bbox <- c(
      min_longitude = input$min_longitude,
      min_latitude = input$min_latitude,
      max_longitude = input$max_longitude,
      max_latitude = input$max_latitude
    )

    occ_search_result <- search_occurrences(bbox, clade)

    leaflet() %>%
      addTiles() %>%
      addCircleMarkers(
        data = occ_search_result$data,
        lng = ~decimalLongitude,
        lat = ~decimalLatitude,
        popup = ~species,
        radius = 5,
        color = "blue",
        fillOpacity = 0.7
      ) %>%
      setView(
        lng = mean(bbox[c("min_longitude", "max_longitude")]),
        lat = mean(bbox[c("min_latitude", "max_latitude")]),
        zoom = 14
      )
  })
}

#et voilà! You can run the application
shinyApp(ui = ui, server = server)

Continue reading: Simple and Fast Visualization of Biodiversity Occurrence Data using GBIF and R Shiny

Implications and Future Developments of Biodiversity Occurrence Visualization Using GBIF and R Shiny

The use of the Global Biodiversity Information Facility (GBIF) and R Shiny for biodiversity occurrence data visualization is a vital tool in understanding species distribution patterns and ecological requirements. The insights this kind of data visualization offers are critical for understanding biodiversity dynamics both in space and time. Furthermore, such visualization is extremely useful in strategic planning of sampling and monitoring activities in the field, through optimized resource and time allocation.

Long-term Implications

Implementing R Shiny apps to collect data from GBIF can have broad, long-term implications for ecological research and conservation. Being able to easily access and visualize over 2 billion records of species occurrence globally helps scientists monitor species distributions over space and time. This can be critical in detecting biodiversity changes due to climate change, habitat destruction, or invasive species. The app can be particularly useful for conservation ecologists who are planning fieldwork in new or remote areas, as it can help decide the best areas to sample based on known species occurrences.

Potential Future Developments

While the current system is already beneficial, there is room for significant improvements in the future. Here are a few potential developments:

• Expanding the polygon specifications: Currently, this app only uses minimum and maximum latitude and longitude to define a polygon, restricting its use to a quadrilateral. In the future, more complex polygons could be used to better restrict searches to the actual area of interest.
• Including more filtering options: Being able to filter the data by additional parameters, such as date, would be useful. This could allow for more specific temporal analyses, such as species migration tracking.
• Enhancing the data presentation: Currently, the app displays the data in an interactive map with basic information. More detailed results, that possibly include the introduction of graphs or charts for data visualization, could be beneficial for complex analyses.

Actionable Advice

Overall, using this R Shiny app for accessing GBIF data offers a great opportunity for researchers and biodiversity enthusiasts to visualize global biodiversity occurrence data. To make the most of this app, users should:

• Ensure they have a good understanding of R programming to customize the data retrieval and visualization to better suit their specific needs.
• Regularly keep the app updated with the latest data from GBIF to have the most recent information available for any analysis.
• Consider the potential future developments and how they might be implemented into the application to enhance usability and precision.

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