Tripneustes ventricosus

Tripneustes ventricosus have a broad distribution, from south Florida and Bermuda up to the south of Brazil, inhabiting various habitats. It can exert significant grazing pressure on coral reefs, although it usually has a more significant impact on seagrass beds.

White sea urchin. Cifonauta image database. Available at: http://cifonauta.cebimar.usp.br/media/9531/ Accessed: 2024-08-12. CC BY-NC-SA 3.0 DEED

Current and future distribution (SDMs)

Both E. lucunter and T. ventricosus showed areas of higher suitability in the Caribbean and south of the Gulf of Mexico, especially along the coast of Campeche to Quintana Roo (Mexico). Both species also show areas of high suitability along the Antilles. Tripneustes ventricosus seems to be less tolerant to colder temperatures.

Code

suppressPackageStartupMessages(library(terra))
suppressPackageStartupMessages(library(sf))
library(leaflet)
library(leaflet.providers)
library(leafem)

sp <- "trve"

basedir <- paste0("../results/", sp, "/predictions/")

sdm_proj <- list.files(basedir)
sdm_proj <- sdm_proj[grepl("mean", sdm_proj)]
sdm_proj_cont <- sdm_proj[grepl("cont", sdm_proj)]

proj_lays <- rast(paste0(basedir, sdm_proj_cont))
proj_lays <- project(proj_lays, "EPSG:3857")

# Normalize to 0-1
proj_lays <- (proj_lays - min(terra::minmax(proj_lays$trve_mean_m6_cont_current)[1,])) / (terra::minmax(proj_lays$trve_mean_m6_cont_current)[2,] - terra::minmax(proj_lays$trve_mean_m6_cont_current)[1,])

# Get areas of extrapolation
extrap_lays <- proj_lays[[2:4]]
extrap_lays[extrap_lays <= terra::minmax(proj_lays$trve_mean_m6_cont_current)[2,]] <- NA
extrap_lays[!is.na(extrap_lays)] <- 1

extrap_shape <- lapply(1:3, function(id){
  terra::project(terra::as.polygons(extrap_lays[[id]]), "EPSG:4326")
})

# Set maximum to the maximum of current layer
proj_lays[proj_lays > 1] <- 1

# Load points
pts <- read.csv(paste0("../data/", sp, "/", sp, "_filt.csv"))
pts <- vect(pts, geom = c("x", "y"), crs = crs(rast(paste0(basedir, sdm_proj_cont[1]))))
pts <- project(pts, "EPSG:4326")
pts <- as.data.frame(geom(pts))

# Plot maps
leaflet() %>%
  #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>%
  addRasterImage(
    proj_lays[[1]],
    project = F,
    colors = colorNumeric(
      palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291",
                        "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")),
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "Current"
  ) %>%
  addRasterImage(
    proj_lays[[2]],
    project = F,
    colors = colorNumeric(
      palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291",
                        "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")),
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP1 (RCP2.6)"
  ) %>%
  addPolygons(data = extrap_shape[[1]], group = "SSP1 (RCP2.6)") %>%
  addRasterImage(
    proj_lays[[3]],
    project = F,
    colors = colorNumeric(
      palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291",
                        "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")),
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP2 (RCP4.5)"
  ) %>%
  addPolygons(data = extrap_shape[[2]], group = "SSP2 (RCP4.5)") %>%
  addRasterImage(
    proj_lays[[4]],
    project = F,
    colors = colorNumeric(
      palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291",
                        "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")),
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP3 (RCP7.0)"
  ) %>%
  addPolygons(data = extrap_shape[[3]], group = "SSP3 (RCP7.0)") %>%
  addLegend(pal = colorNumeric(
      palette = c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291",
                        "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695"),
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ), values = values(proj_lays[[1]]), title = "ROR", opacity = 1, position = "bottomright",
    labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE))) %>%
  addCircleMarkers(lng = pts$x, lat = pts$y,
                   #clusterOptions = markerClusterOptions(),
                   radius = 5, weight = 2.5,
                   group = "Occurrence") %>%
  addLayersControl(
    baseGroups = c("Current", "SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"),
    overlayGroups = c("Occurrence"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  setView(-60, 0, zoom=3)

Changes in future distribution (SDMs)

Tripneustes ventricosus do not present any apparent loss in its distribution range compared to the current scenario. This species would increase its range of suitable areas to the north and to the south.

Code

delta <- proj_lays[[2:4]] - proj_lays[[1]]

# Plot maps
leaflet() %>%
  #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>%
  addRasterImage(
    delta[[1]],
    project = F,
    colors = colorNumeric(
      palette = "BrBG",
      domain = c(-1,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP1 (RCP2.6)"
  ) %>%
  addRasterImage(
    delta[[2]],
    project = F,
    colors = colorNumeric(
      palette = "BrBG",
      domain = c(-1,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP2 (RCP4.5)"
  ) %>%
  addRasterImage(
    delta[[3]],
    project = F,
    colors = colorNumeric(
      palette = "BrBG",
      domain = c(-1,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP3 (RCP7.0)"
  ) %>%
  addLegend(pal = colorNumeric(
      palette = "BrBG",
      domain = c(-1,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = T
    ), values = seq(-1, 1, by = 0.1), title = "Delta ROR", opacity = 1, position = "bottomright",
    labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE))) %>%
  addCircleMarkers(lng = pts$x, lat = pts$y,
                   #clusterOptions = markerClusterOptions(),
                   radius = 5, weight = 2.5,
                   group = "Occurrence") %>%
  addLayersControl(
    baseGroups = c("SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"),
    overlayGroups = c("Occurrence"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  setView(-60, 0, zoom=3)

Current and future distribution (mechanistic model)

Tripneustes ventricosus had a smaller suitable area than the other species. It was more concentrated in tropical areas mainly from Florida to the south of Rio de Janeiro. The loss of suitable areas in the future was proportionally similar to the other species (12, 47 and 65% in the SSP1, SSP2, and SSP3 scenarios respectively), but T. ventricosus suitable area was ~20% smaller than the one of L. variegatus and ~12% smaller than the suitable area of E. lucunter.

Code

# Load layers and prepare
# Load threshold data ----
load("../data/sst_limits/allspecies_oisst_thvalues.RData")

# Load results ----
sp <- "trve" # Each species is run separately [try "eclu" and "trve"]

# Load rasters generated before
curr <- rast(paste0("../data/sst_limits/", sp, "_current_thresh.tif"))
ssp1 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp126", "_thresh.tif"))
ssp2 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp245", "_thresh.tif"))
ssp3 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp370", "_thresh.tif"))

curr <- project(curr, "EPSG:3857")
ssp1 <- project(ssp1, "EPSG:3857")
ssp2 <- project(ssp2, "EPSG:3857")
ssp3 <- project(ssp3, "EPSG:3857")

# Get the percentage of time to use as threshold (mean of min and max point)
lval <- round(((thresholds[[sp]]$time_inrange_hottest_point +
                        thresholds[[sp]]$time_inrange_coolest_point)/2),
              2) # round to 2 digits


# Get the polygons of the areas that are suitable
get.pol <- function(x){
        # temp <- terra::app(x, function(x){
        #         x[x < lval] <- NA
        #         x[x >= lval] <- 1
        #         x
        # })
        # temp <- as.polygons(temp)
        # temp <- aggregate(buffer(temp,0.0001)) # We use a negligible value here
        #                                       # to solve problems in the pols
        #                                       # conversion.
        # temp <- project(temp, "EPSG:4326")
        # # temp <- st_as_sf(temp)
        # # temp <- st_set_crs(temp, crs("EPSG:4326"))
        # temp
      x[x < lval] <- NA
      x[x >= lval] <- 1
      terra::project(terra::as.polygons(x), "EPSG:4326")
}

curr.p <- get.pol(curr)
ssp1.p <- get.pol(ssp1)
ssp2.p <- get.pol(ssp2)
ssp3.p <- get.pol(ssp3)



# Plot maps
leaflet() %>%
  #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>%
  addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>%
  addRasterImage(
    curr,
    project = F,
    colors = colorNumeric(
      palette = "Spectral",
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "Current"
  ) %>%
  addPolygons(data = curr.p,
    group = "Current Suitable") %>%
  addRasterImage(
    ssp1,
    project = F,
    colors = colorNumeric(
      palette = "Spectral",
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP1 (RCP2.6)"
  ) %>%
  addPolygons(data = ssp1.p,
    group = "SSP1 Suitable") %>%
  addRasterImage(
    ssp2,
    project = F,
    colors = colorNumeric(
      palette = "Spectral",
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP2 (RCP4.5)"
  ) %>%
  addPolygons(data = ssp2.p,
    group = "SSP2 Suitable") %>%
  addRasterImage(
    ssp3,
    project = F,
    colors = colorNumeric(
      palette = "Spectral",
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = FALSE
    ),
    group = "SSP3 (RCP7.0)"
  ) %>%
  addPolygons(data = ssp3.p,
    group = "SSP3 Suitable") %>%
  addLegend(pal = colorNumeric(
      palette = "Spectral",
      domain = c(0,1),
      na.color = "#00000000",
      alpha = FALSE,
      reverse = TRUE
    ), values = seq(0, 1, by = 0.1), title = "% time", opacity = 1, position = "bottomright",
    labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE) * 100)) %>%
  addCircleMarkers(lng = pts$x, lat = pts$y,
                   #clusterOptions = markerClusterOptions(),
                   radius = 5, weight = 2.5,
                   group = "Occurrence") %>%
  addLayersControl(
    baseGroups = c("Current", "SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"),
    overlayGroups = c("Occurrence", "Current Suitable", "SSP1 Suitable", "SSP2 Suitable", "SSP3 Suitable"),
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(c("Occurrence", "Current Suitable", "SSP1 Suitable", "SSP2 Suitable", "SSP3 Suitable")) %>%
  setView(-60, 0, zoom=3)

--- title: "Tripneustes ventricosus" format: html: code-fold: true code-tools: true --- ::: {.grid} ::: {.g-col-8} _Tripneustes ventricosus_ have a broad distribution, from south Florida and Bermuda up to the south of Brazil, inhabiting various habitats. It can exert significant grazing pressure on coral reefs, although it usually has a more significant impact on seagrass beds. ::: ::: {.g-col-4} ![](images/trve_cifonauta.jpg) White sea urchin. Cifonauta image database. Available at: http://cifonauta.cebimar.usp.br/media/9531/ Accessed: 2024-08-12. CC BY-NC-SA 3.0 DEED ::: ::: ## Current and future distribution (SDMs) Both _E. lucunter_ and _T. ventricosus_ showed areas of higher suitability in the Caribbean and south of the Gulf of Mexico, especially along the coast of Campeche to Quintana Roo (Mexico). Both species also show areas of high suitability along the Antilles. _Tripneustes ventricosus_ seems to be less tolerant to colder temperatures. ```{r} #| column: screen #| warning: false suppressPackageStartupMessages(library(terra)) suppressPackageStartupMessages(library(sf)) library(leaflet) library(leaflet.providers) library(leafem) sp <- "trve" basedir <- paste0("../results/", sp, "/predictions/") sdm_proj <- list.files(basedir) sdm_proj <- sdm_proj[grepl("mean", sdm_proj)] sdm_proj_cont <- sdm_proj[grepl("cont", sdm_proj)] proj_lays <- rast(paste0(basedir, sdm_proj_cont)) proj_lays <- project(proj_lays, "EPSG:3857") # Normalize to 0-1 proj_lays <- (proj_lays - min(terra::minmax(proj_lays$trve_mean_m6_cont_current)[1,])) / (terra::minmax(proj_lays$trve_mean_m6_cont_current)[2,] - terra::minmax(proj_lays$trve_mean_m6_cont_current)[1,]) # Get areas of extrapolation extrap_lays <- proj_lays[[2:4]] extrap_lays[extrap_lays <= terra::minmax(proj_lays$trve_mean_m6_cont_current)[2,]] <- NA extrap_lays[!is.na(extrap_lays)] <- 1 extrap_shape <- lapply(1:3, function(id){ terra::project(terra::as.polygons(extrap_lays[[id]]), "EPSG:4326") }) # Set maximum to the maximum of current layer proj_lays[proj_lays > 1] <- 1 # Load points pts <- read.csv(paste0("../data/", sp, "/", sp, "_filt.csv")) pts <- vect(pts, geom = c("x", "y"), crs = crs(rast(paste0(basedir, sdm_proj_cont[1])))) pts <- project(pts, "EPSG:4326") pts <- as.data.frame(geom(pts)) # Plot maps leaflet() %>% #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>% addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>% addRasterImage( proj_lays[[1]], project = F, colors = colorNumeric( palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")), domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "Current" ) %>% addRasterImage( proj_lays[[2]], project = F, colors = colorNumeric( palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")), domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP1 (RCP2.6)" ) %>% addPolygons(data = extrap_shape[[1]], group = "SSP1 (RCP2.6)") %>% addRasterImage( proj_lays[[3]], project = F, colors = colorNumeric( palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")), domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP2 (RCP4.5)" ) %>% addPolygons(data = extrap_shape[[2]], group = "SSP2 (RCP4.5)") %>% addRasterImage( proj_lays[[4]], project = F, colors = colorNumeric( palette = rev(c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695")), domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP3 (RCP7.0)" ) %>% addPolygons(data = extrap_shape[[3]], group = "SSP3 (RCP7.0)") %>% addLegend(pal = colorNumeric( palette = c("#A84C00", "#D97D27", "#F5BD44", "#FFD561", "#FFF291", "#FFFFBF", "#E0F3F8", "#ABD9E9", "#74ADD1", "#4575B4", "#313695"), domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), values = values(proj_lays[[1]]), title = "ROR", opacity = 1, position = "bottomright", labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE))) %>% addCircleMarkers(lng = pts$x, lat = pts$y, #clusterOptions = markerClusterOptions(), radius = 5, weight = 2.5, group = "Occurrence") %>% addLayersControl( baseGroups = c("Current", "SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"), overlayGroups = c("Occurrence"), options = layersControlOptions(collapsed = FALSE) ) %>% setView(-60, 0, zoom=3) ``` ## Changes in future distribution (SDMs) _Tripneustes ventricosus_ do not present any apparent loss in its distribution range compared to the current scenario. This species would increase its range of suitable areas to the north and to the south. ```{r} #| column: screen delta <- proj_lays[[2:4]] - proj_lays[[1]] # Plot maps leaflet() %>% #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>% addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>% addRasterImage( delta[[1]], project = F, colors = colorNumeric( palette = "BrBG", domain = c(-1,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP1 (RCP2.6)" ) %>% addRasterImage( delta[[2]], project = F, colors = colorNumeric( palette = "BrBG", domain = c(-1,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP2 (RCP4.5)" ) %>% addRasterImage( delta[[3]], project = F, colors = colorNumeric( palette = "BrBG", domain = c(-1,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP3 (RCP7.0)" ) %>% addLegend(pal = colorNumeric( palette = "BrBG", domain = c(-1,1), na.color = "#00000000", alpha = FALSE, reverse = T ), values = seq(-1, 1, by = 0.1), title = "Delta ROR", opacity = 1, position = "bottomright", labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE))) %>% addCircleMarkers(lng = pts$x, lat = pts$y, #clusterOptions = markerClusterOptions(), radius = 5, weight = 2.5, group = "Occurrence") %>% addLayersControl( baseGroups = c("SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"), overlayGroups = c("Occurrence"), options = layersControlOptions(collapsed = FALSE) ) %>% setView(-60, 0, zoom=3) ``` ## Current and future distribution (mechanistic model) _Tripneustes ventricosus_ had a smaller suitable area than the other species. It was more concentrated in tropical areas mainly from Florida to the south of Rio de Janeiro. The loss of suitable areas in the future was proportionally similar to the other species (12, 47 and 65% in the SSP1, SSP2, and SSP3 scenarios respectively), but T. ventricosus suitable area was ~20% smaller than the one of _L. variegatus_ and ~12% smaller than the suitable area of _E. lucunter_. ```{r} #| column: screen # Load layers and prepare # Load threshold data ---- load("../data/sst_limits/allspecies_oisst_thvalues.RData") # Load results ---- sp <- "trve" # Each species is run separately [try "eclu" and "trve"] # Load rasters generated before curr <- rast(paste0("../data/sst_limits/", sp, "_current_thresh.tif")) ssp1 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp126", "_thresh.tif")) ssp2 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp245", "_thresh.tif")) ssp3 <- rast(paste0("../data/sst_limits/", sp, "_", "ssp370", "_thresh.tif")) curr <- project(curr, "EPSG:3857") ssp1 <- project(ssp1, "EPSG:3857") ssp2 <- project(ssp2, "EPSG:3857") ssp3 <- project(ssp3, "EPSG:3857") # Get the percentage of time to use as threshold (mean of min and max point) lval <- round(((thresholds[[sp]]$time_inrange_hottest_point + thresholds[[sp]]$time_inrange_coolest_point)/2), 2) # round to 2 digits # Get the polygons of the areas that are suitable get.pol <- function(x){ # temp <- terra::app(x, function(x){ # x[x < lval] <- NA # x[x >= lval] <- 1 # x # }) # temp <- as.polygons(temp) # temp <- aggregate(buffer(temp,0.0001)) # We use a negligible value here # # to solve problems in the pols # # conversion. # temp <- project(temp, "EPSG:4326") # # temp <- st_as_sf(temp) # # temp <- st_set_crs(temp, crs("EPSG:4326")) # temp x[x < lval] <- NA x[x >= lval] <- 1 terra::project(terra::as.polygons(x), "EPSG:4326") } curr.p <- get.pol(curr) ssp1.p <- get.pol(ssp1) ssp2.p <- get.pol(ssp2) ssp3.p <- get.pol(ssp3) # Plot maps leaflet() %>% #addProviderTiles("OpenStreetMap.Mapnik", group = "OSM") %>% addProviderTiles("Esri.WorldGrayCanvas", group = "ESRI Gray") %>% addRasterImage( curr, project = F, colors = colorNumeric( palette = "Spectral", domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "Current" ) %>% addPolygons(data = curr.p, group = "Current Suitable") %>% addRasterImage( ssp1, project = F, colors = colorNumeric( palette = "Spectral", domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP1 (RCP2.6)" ) %>% addPolygons(data = ssp1.p, group = "SSP1 Suitable") %>% addRasterImage( ssp2, project = F, colors = colorNumeric( palette = "Spectral", domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP2 (RCP4.5)" ) %>% addPolygons(data = ssp2.p, group = "SSP2 Suitable") %>% addRasterImage( ssp3, project = F, colors = colorNumeric( palette = "Spectral", domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = FALSE ), group = "SSP3 (RCP7.0)" ) %>% addPolygons(data = ssp3.p, group = "SSP3 Suitable") %>% addLegend(pal = colorNumeric( palette = "Spectral", domain = c(0,1), na.color = "#00000000", alpha = FALSE, reverse = TRUE ), values = seq(0, 1, by = 0.1), title = "% time", opacity = 1, position = "bottomright", labFormat = labelFormat(transform = function(x) sort(x, decreasing = TRUE) * 100)) %>% addCircleMarkers(lng = pts$x, lat = pts$y, #clusterOptions = markerClusterOptions(), radius = 5, weight = 2.5, group = "Occurrence") %>% addLayersControl( baseGroups = c("Current", "SSP1 (RCP2.6)", "SSP2 (RCP4.5)", "SSP3 (RCP7.0)"), overlayGroups = c("Occurrence", "Current Suitable", "SSP1 Suitable", "SSP2 Suitable", "SSP3 Suitable"), options = layersControlOptions(collapsed = FALSE) ) %>% hideGroup(c("Occurrence", "Current Suitable", "SSP1 Suitable", "SSP2 Suitable", "SSP3 Suitable")) %>% setView(-60, 0, zoom=3) ```