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Inferring extirpation in potential habitat
Example I

Example I

This map plots this mean per-cell posterior extirpation likelihood for Primula pauciflora over both historical and potential habitat.

prim_accepted_sf <- st_read("Analysis_outputs/Primula pauciflora_Solow_low.shp", quiet=TRUE)
# Filter for region
# Convert from mean likelihood of presence to mean likelihood of extirpation
prim_accepted_sf$mean_ep <- 1 - prim_accepted_sf$mean

pal <- colorNumeric(palette = "viridis", domain = range(c(0, prim_accepted_sf$mean_ep), na.rm = TRUE))
m <- leaflet(data = prim_accepted_sf) %>%
  # Add a Tiles layer to the map
  addProviderTiles("Esri.WorldImagery") %>%
  # Add the grid layer to the map
  addPolygons(fillColor = ~pal(mean_ep), fillOpacity = 0.8, 
              color = "#BDBDC3", weight = 1) %>%
  # Add a legend
  addLegend(pal = pal, values = c(0, max(prim_accepted_sf$mean_ep, na.rm = TRUE)),
            opacity = 0.8, title = "Mean likelihood of extirpation")

# Print the map
m

Pooling these distributions as before produces posterior regional statistics for extirpation across all habitat as follows:

These show the computed parameters of the posterior beta distribution for extirpation expressed in two different forms - the standard (alpha, beta) representation and (mu, var) as parameters for the central estimate for sighting probability and its dispersion. Confidence bands are placed for this at [64.9%, 100.0%].

Here is this posterior distribution graphed out - the central estimate of extirpation probability is 83.7%.

Kernel validationExample II