Mapping the Distribution of Ficus aurea and Ficus citrifolia individuals in Florida State Parks

Visualizing the distribution of Ficus aurea and Ficus citrifolia collections across Florida State Parks using R spatial analysis tools.
Biodiversity
Spatial Analysis
R
Author

Kevin Quinteros

Published

July 18, 2025

Overview

I recently had to plot the locality data from our field work done in Florida State Park. Using R’s spatial data visualization tools, I combined occurrence records, park boundaries, and elevation data to show the locality of individuals that were geo-referenced.

Data Sources

I used shapefiles of the state of Florida and its state park boundaries. These shapefiles are publicly available, and I have provided the links below for anyone who wishes to use them.

Input Data:

  1. Individual coordinate data.

  2. Detailed_Florida_State_Boundary.shp: High-resolution Florida boundary shapefile.

  3. Florida_State_Parks_Boundaries.shp: Shapefiles for all Florida State Parks.

Output:

  • maps visualizing collection distributions.

R Packages Used

Code 
library(tidyverse)
library(ggplot2)
library(cowplot)
library(sf)
library(wesanderson)

Loading and Cleaning Occurrence Data

I have preloaded my data. You can load your own data or the occurrence data provided in the GitHub folder associated with this post

Reading layer `Florida_State_Parks_Boundaries' from data source 
  `/Users/kq/Projects/kquinteros.github.io/posts/2025-10-27-map-dist/data/Florida_State_Parks_Boundaries/Florida_State_Parks_Boundaries.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 178 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 65060.63 ymin: 62239.01 xmax: 793312 ymax: 758319.8
Projected CRS: NAD83(2011) / Florida GDL Albers
Reading layer `Detailed_Florida_State_Boundary' from data source 
  `/Users/kq/Projects/kquinteros.github.io/posts/2025-10-27-map-dist/data/Detailed_Florida_State_Boundary/Detailed_Florida_State_Boundary.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -134968.2 ymin: 2712255 xmax: 596284 ymax: 3448075
Projected CRS: NAD83 / UTM zone 17N

To start, we’ll tidy up the data and pick the state parks where we want to visualize our occurrence records.

Code 
# select ficus occurrence that occur in state park
species_occ <- species_occ[species_occ$Locality %in% c(
  "Fakahatchee State Park", "Estero Bay", "Dr. Von D. Mizell State Park",
  "Hugh Taylor Birch State Park", "Bill Baggs state Park",
  "Oleta River State Park", "Charlotte Harbor Preserve State Park"
),]

#clean up species names and edit state park names in our occurence csv so they match the official state park titles.
species_occ <- species_occ |>
  mutate(Species = case_when(
    Species == "Aurea/Citrifolia?" ~ "Hybrid",
    Species == "Aurea?" ~ "Unknown",
    TRUE ~ Species
  )) |>
  mutate(Locality = case_when(
    Locality == "Fakahatchee State Park" ~ "Fakahatchee Strand Preserve State Park",
    Locality == "Estero Bay" ~ "Estero Bay Preserve State Park",
    Locality == "Bill Baggs state Park" ~ "Bill Baggs Cape Florida State Park",
    Locality == "Dr. Von D. Mizell State Park" ~ "Dr. Von D. Mizell-Eula Johnson State Park",
    TRUE ~ Locality
  ))

Spatial Data for Florida State and Parks

Next, we have to subset our Florida state parks shape files. Additionally, we don’t need to show the entire state of Florida, so we will crop the shapefile to focus only on southern Florida.

Code 
#subset our shape files. 
fl_parks <- fl_parks |> filter(SITE_NAME %in% c(
  "Charlotte Harbor Preserve State Park", "Estero Bay Preserve State Park",
  "Fakahatchee Strand Preserve State Park", "Bill Baggs Cape Florida State Park",
  "Dr. Von D. Mizell-Eula Johnson State Park", "Hugh Taylor Birch State Park",
  "Oleta River State Park"
))
#transform data
state <- st_transform(state, 4326)

#crop our Florida state park to only show southern Florida instead of the whole state.
south_florida_bbox <- st_bbox(c(xmin = -82.9, ymin = 24.9, xmax = -79.8, ymax = 27.6), crs = st_crs(state))
south_florida <- st_crop(state, st_as_sfc(south_florida_bbox))
Warning: attribute variables are assumed to be spatially constant throughout
all geometries

Mapping the Distribution

Now, we are ready to plot our data

Code 
ggplot() +
  geom_sf(data = south_florida, fill = "antiquewhite", color = "black") + #shapefile for south Florida)
  geom_sf(data = fl_parks, fill = "forestgreen", alpha = 0.5, color = "darkgreen") +  #shape file for florida state parks
  geom_point(data = species_occ, aes(x = Lon, y = Lat, fill = Species), shape=21, color="black", size=1) + # ficus occurrence data
  labs(title = "Ficus Occurrences in Florida State Parks") + # title
  scale_fill_manual(values = wes_palette("Darjeeling1")) + # using the wes anderson color palette here
  coord_sf(xlim = c(-82.7, -79.8), ylim = c(24.8, 27.5), expand = FALSE) + # adjust the size of the map
  theme_cowplot() + 
  theme(panel.background = element_rect(fill = "aliceblue"), # all the setting below adjust title and other plot properties
        plot.title = element_text(hjust = 0.5, face = "bold", size = 10),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_rect(colour = "black", fill=NA, size=1.5),
        text = element_text(size = 10))
Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
i Please use the `linewidth` argument instead.

Individual Park Maps

Now we have a plot showing Ficus occurrences across all the state parks where we collected data. However, we also want to visualize the occurrences for individual parks. Instead of writing the same code for each park, we can automate this process by creating a loop.

Code 
for (park in unique(fl_parks$SITE_NAME)) { #start the loop
  park_poly <- fl_parks[fl_parks$SITE_NAME == park, ] #select the park where we are mapping
  park_occ  <- species_occ[species_occ$Locality == park, ] #select individual that were collected in that park
  if (nrow(park_poly) == 0 || st_is_empty(park_poly) || !st_is_valid(park_poly)) next # check that our shapefile is not empty
  if (st_crs(park_poly) != st_crs(state)) park_poly <- st_transform(park_poly, crs = st_crs(state))
  safe_name <- str_replace_all(park, "[^A-Za-z0-9]", "_")
  bbox <- st_bbox(park_poly)
  pad <- 0.01 # padding parameter f
  xlim <- c(as.numeric(bbox$xmin) - pad, as.numeric(bbox$xmax) + pad) # create a small margin for the graph based on shapefile of park
  ylim <- c(as.numeric(bbox$ymin) - pad, as.numeric(bbox$ymax) + pad)
  print(
    ggplot() +
      geom_sf(data = state, fill = "antiquewhite", color = "black") +
      geom_sf(data = park_poly, fill = "forestgreen", alpha = 0.6, color = "darkgreen") +
      geom_point(data = park_occ, aes(x = Lon, y = Lat, fill = Species), shape=21, color="black", size=1) +
      coord_sf(xlim = xlim, ylim = ylim) +
      theme_cowplot() +
      labs(title = park) +
      scale_fill_manual(values = wes_palette("Darjeeling1"))
  )
}