Exploring Zonation outputs • ZonationR

This vignette demonstrates how to explore and interpret some of the main outputs generated by Zonation using the ZonationR package. Specifically, users will:

Examine spatial patterns of conservation priorities.
Inspect performance curves.
Assess the distribution of feature coverage.

Setup

Installation

# Install required packages if not already installed
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}

if (!requireNamespace("patchwork", quietly = TRUE)) {
  install.packages("patchwork")
}

Libraries

# Load necessary libraries
library(ZonationR)
library(Zonation5RData)
library(ggplot2)
library(patchwork)

Prepare input data

Here, we use the baseline variant output folder from the vignette Variants worflow to illustrate some of the post-processing functions. You can either use the output folder data provided by the Zonation5RData package, as shown below, or, if you are following the multiple variants tutorial, you can use any output folder from there.

# Create a local copy of the example baseline folder
src <- zonation5rdata_path("01_baseline")

# Copy the whole directory, preserving the structure
file.copy(src, ".", recursive = TRUE, overwrite = TRUE)

# Define a variable for the baseline folder
baseline_folder <- "01_baseline"

Priority map

One of the main outputs of Zonation is the priority rank map, which shows the conservation priority of each cell across the landscape. Higher numbers indicate areas that are more important for conservation, while lower numbers indicate areas of lower priority. Each rank value tells you how a cell compares to others in terms of conservation priority. But please note that the scale is relative, not absolute. For example, a cell ranked 0.8 is not twice as valuable as one ranked 0.4. The priority map should then be considered together with other outputs, like performance curves, to fully understand the spatial distribution of conservation value, potential trade-offs, and the conservation effectiveness. We can use the priority_map() function to visualize the ranking values across the landscape:

p1 <- priority_map(baseline_folder)

print(p1)

In addition to visualizing the continuous priority values, we can also adjust the map to make important areas stand out more clearly. The classify argument in priority_map() allows choosing whether to display the map as continuous or divided into classes. If classify = TRUE, the map is divided into discrete classes, and we can define custom breaks and labels to make it easier to see which areas have higher or lower priority.

breaks <- c(0, 0.1, 0.5, 0.9, 1)
labels <- c("very low", "low", "medium", "very high")
p2 <- priority_map(baseline_folder, classify = TRUE,
                   breaks = breaks, labels = labels)
print(p2)

The visualization functions in ZonationR return ggplot2 objects, which means that users can further customize the plots using the ggplot2 ecosystem. For example, we can apply a different color palette to the map:

p2 <- p2 + scale_fill_viridis_d() + labs(fill = "rank")
print(p2)

Performance curves

The performance curves describe how much of each feature’s distribution would be covered if we protected grid cells following their priority rank order. The summary_curves() function is helpful for understanding the overall performance of a solution. It allows us to explore key metrics such as:

mean - average performance across all species
max - performance of the best-performing species
min - performance of the worst-performing species

p3 <- summary_curves(baseline_folder, metrics = c("mean", "max", "min")) +
  ggtitle("Summary Curves") +
  theme(plot.title = element_text(hjust = 0.5))

print(p3)

We can also visualize additional metrics from the summary curves file using separate panels (facets). The facet argument should be set to TRUE when plotting metrics that have different units or value ranges.

p4 <- summary_curves(baseline_folder,
                     metrics = c("remaining_area", "mean"),
                     facet = TRUE)
print(p4)

Besides looking at overall performance with summary_curves(), one could also be interested in how each feature is represented across the priority ranking, which can be explored using the feature_curves() function:

p5 <- feature_curves(baseline_folder) +
  ggtitle("Feature curves") +
  theme(plot.title = element_text(hjust = 0.5))

print(p5)

As with priority_map(), the functions summary_curves() and feature_curves() return ggplot2 objects, allowing further customization of the plots. For example, we can add a vertical line to highlight the top 10% (rank = 0.9) priority cells.

p5 <- p5 + geom_vline(xintercept = 0.9, linetype = "dashed", color = "red")

print(p5)

The vertical line at 0.9 represents the top 10% priority areas. Intersection with the curves shows the proportion of each feature representation within these areas.

As mentioned earlier, priority rank maps and performance curves should be interpreted together. For example, we could use the priority_map() function to plot a classified map showing only the top 10% priority areas, alongside the corresponding feature curves.

# Define the threshold for the top 10% priority cells
threshold <- 0.90

# Define breaks and labels for classified map
breaks <- c(0, 0.90, 1)             # 0-90% = other areas, 90-100% = top 10%
labels <- c("other areas", "top 10%")

# Plot the classified map with two colors
top10_map <- priority_map(
  baseline_folder,
  classify = TRUE,
  breaks = breaks,
  labels = labels
) +
  scale_fill_manual(values = c("lightgray", "darkgreen")) +
  ggtitle("Priority map") +
  theme(plot.title = element_text(hjust = 0.5),
        legend.position = "bottom") + 
  labs(fill = "rank")

# Combine map and feature curves
p1_combined <- top10_map + p5

print(p1_combined)

By combining the classified map and the feature curves side by side, we can see which areas are top-priority and how well each species is represented within those areas (i.e., the curves intersecting the red dashed line), providing a integrated view of spatial priorities and species coverage.

Coverage distribution

In addition to maps and performance curves, we can also explore the coverage distribution for any selected priority fraction using the function coverage_distribution(). The histogram shows, for each coverage level (or coverage bin) on the x-axis, the number of species reaching that coverage level (y-axis). For example, a bar reaching 30 on the y-axis at the coverage range 0.1-0.2 means that 30 species have between 10% and 20% of their range included in the selected priority fraction.

p6 <- coverage_distribution(baseline_folder, target_rank = 0.9) +
  ggtitle("Coverage at the top 10%") +
  theme(plot.title = element_text(hjust = 0.5))

print(p6)

An overview of Zonation results can be created by combining maps and other outputs.

p2_combined <- top10_map + p5 / p6

print(p2_combined)

Key takeaways

By looking at the combined visualization, we can see which areas are top-priority (map), how well each species is represented in those areas (feature curves), and the number of species at different coverage levels (histogram). This provides a full view of spatial priorities and feature-level coverage, illustrating how ZonationR allows users to explore and interpret conservation outcomes in a flexible and straightforward way.