ecosystem_services {ggsankeyfier}

Data on risks to supply ecosystem services

Description

Data indicating a risk resulting from anthropological activities to the marine ecosystem and its capacity to supply services. This data set serves (aggregated from Piet et al. (submitted)) as an example to illustrate the package's features.

Format

ecosystem_services is a data.frame with 3421 rows and 8 columns. The columns are:

• activity_type: Type of activities that pose a risk

• activity_realm: Aggregation of activity types

• pressure_cat: Category of pressures exerted by the activities and eventually pose a risk to the ecosystem.

• biotic_group: Biotic groups affected by the pressures.

• biotic_realm: Aggregation of biotic groups

• service_division: Division of ecosystem services that are provided by the biotic groups and affected by the activities.

• service_section: Aggregation of service divisions.

• RCSES: 'Risk to Capacity to Supply Ecosystem Services'. A numerical score reflecting the amount of risk for the ecosystem to supply specific services. For more details see Piet et al. (submitted)

This data.frame is in a wide oriented format, typical for most common applications. Each row in the data.frame represents a unique pathway where each activity_tpe poses a risk to an ecosystem sevice_division, via a pressure_cat and biotic_group. Each column either contains information on a specific stage or the overall quantifier (in this case RCSES).

In its present form it is not suitable to directly plot as a Sankey diagram. For that purpose it needs to be pivoted with pivot_stages_longer(). Two different variants are prepared with this function: ecosystem_services_pivot1 and ecosystem_services_pivot2.

The latter pivot contains service_section as an extra feature which can be used for additional decoration of a Sankey diagram. It is therefore more detailed than the first alternative.

ecosystem_services_pivot1 is a data.frame with 112 rows and 5 columns. Columns are:

• RCSES: See above at ecosystem_services.

• edge_id: Unique numerical identifier for each edge in a Sankey diagram.

• connector: One of 'from' or 'to', indicating whether we are looking at the start or the end of an edge.

• node: A collection of activity_realm, pressure_cat, biotic_realm and service_section values from the ecosystem_services data.frame.

• stage: Stages in a Sankey diagram formed by the columns activity_realm, pressure_cat, biotic_realm and service_section from the ecosystem_services data.frame.

ecosystem_services_pivot1 is created from ecosystem_services using pivot_stages_longer() and can be used directly in a Sankey diagram (using geom_sankeynode() and geom_sankeyedge())

ecosystem_services_pivot2 is a data.frame with 252 rows and 6. It is the same as ecosystem_services_pivot1 with the exception of a distinct extra column service_division which allows for more detailed aesthetics in a Sankey diagram.

Author(s)

Pepijn de Vries, Gerjan Piet, Jacob Bentley, Ruud Jongbloed, Anne Grundlehner, Jacqueline Tamis

References

Piet GJ, Bentley J, Jongbloed RH, Grundlehner A, Tamis JE, De Vries P (submitted) A Cumulative Impact Assessment on the North Sea Capacity to Supply Ecosystem Services. doi:10.2139/ssrn.4450241

Examples

data("ecosystem_services")
library(ggplot2)

if (requireNamespace("stringr")) {
  library(stringr)

  pos <- position_sankey(v_space = "auto", align = "justify")
  pos_text <- position_sankey(v_space = "auto", align = "justify", nudge_x = 0.1)

  ## A simplified version of the Sankey diagram as published by Piet _et al._ (submitted)
  ggplot(ecosystem_services |>
           pivot_stages_longer(
             c("activity_type", "pressure_cat", "biotic_group", "service_section"),
             "RCSES"),
         aes(x = stage, y = RCSES, group = node, connector = connector, edge_id = edge_id)) +
    geom_sankeyedge(aes(fill = RCSES), position = pos) +
    geom_sankeynode(position = pos) +
    geom_text(aes(label = str_wrap(node, 20)), position = pos_text, stat = "sankeynode",
              hjust = 0, cex = 2) +
    scale_fill_viridis_c(option = "turbo", trans = "sqrt") +
    theme_minimal()
}