Echinometra lucunter

Two species of Echinometra occur in the western Atlantic: E. viridis and E. lucunter. Echinometra lucunter is one of the most common sea urchins on Brazilian reefs, where it can attain great abundance in shallower areas. Although its importance in herbivory can be variable, species of this genus participate actively in the reef carbonate cycle.

Alvaro E. Migotto. Black sea urchin. Cifonauta image database. Available at: http://cifonauta.cebimar.usp.br/media/4032/ Accessed: 2024-08-12.

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. Echinometra lucunter has a higher range of distribution to the south.

Code

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

sp <- "eclu"

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$eclu_mean_m4_cont_current)[1,])) / (terra::minmax(proj_lays$eclu_mean_m4_cont_current)[2,] - terra::minmax(proj_lays$eclu_mean_m4_cont_current)[1,])

# Get areas of extrapolation
extrap_lays <- proj_lays[[2:4]]
extrap_lays[extrap_lays <= terra::minmax(proj_lays$eclu_mean_m4_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)

Echinometra lucunter 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)

Echinometra lucunter had a similar distribution than L. variegatus, but a smaller extension to the south and unsuitable areas in the northern portion of the Gulf of Mexico. Echinometra lucunter is expected to lose approximately 13, 48, and 65% of its suitable area in the SSP1, SSP2, and SSP3 scenarios.

Code

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

# Load results ----
sp <- "eclu" # 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: "Echinometra lucunter" format: html: code-fold: true code-tools: true --- ::: {.grid} ::: {.g-col-8} Two species of _Echinometra_ occur in the western Atlantic: _E. viridis_ and _E. lucunter_. _Echinometra lucunter_ is one of the most common sea urchins on Brazilian reefs, where it can attain great abundance in shallower areas. Although its importance in herbivory can be variable, species of this genus participate actively in the reef carbonate cycle. ::: ::: {.g-col-4} ![](images/eclu_cifonauta.jpg) Alvaro E. Migotto. Black sea urchin. Cifonauta image database. Available at: http://cifonauta.cebimar.usp.br/media/4032/ Accessed: 2024-08-12. ::: ::: ## 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. _Echinometra lucunter_ has a higher range of distribution to the south. ```{r} #| column: screen #| warning: false suppressPackageStartupMessages(library(terra)) suppressPackageStartupMessages(library(sf)) library(leaflet) library(leaflet.providers) library(leafem) sp <- "eclu" 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$eclu_mean_m4_cont_current)[1,])) / (terra::minmax(proj_lays$eclu_mean_m4_cont_current)[2,] - terra::minmax(proj_lays$eclu_mean_m4_cont_current)[1,]) # Get areas of extrapolation extrap_lays <- proj_lays[[2:4]] extrap_lays[extrap_lays <= terra::minmax(proj_lays$eclu_mean_m4_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) _Echinometra lucunter_ 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) _Echinometra lucunter_ had a similar distribution than _L. variegatus_, but a smaller extension to the south and unsuitable areas in the northern portion of the Gulf of Mexico. _Echinometra lucunter_ is expected to lose approximately 13, 48, and 65% of its suitable area in the SSP1, SSP2, and SSP3 scenarios. ```{r} #| column: screen # Load layers and prepare # Load threshold data ---- load("../data/sst_limits/allspecies_oisst_thvalues.RData") # Load results ---- sp <- "eclu" # 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) ```