Study of biodiversity progression through Living Planet Index

by Carlota Alcoz

Some information on the instalation and loading of packages:

Show the code 
# Required packages
packages <- c("data.table", "fst", "ggplot2", "dplyr", "tidyr", "knitr", "plotly","DT")

#Installing packages if not already installed
to_install <- packages[which(x = !(packages %in% installed.packages()[,"Package"]))]
if(length(to_install) != 0) {
  install.packages(to_install)
}

# Loading packages
temp <- lapply(packages, library, 
               character.only = TRUE)

# Cleaning up the environment
rm(temp, packages, to_instal)

Fetching the data

Show the code 
lpi <- read.csv("https://ourworldindata.org/grapher/global-living-planet-index.csv?v=1&csvType=full&useColumnShortNames=true", 
                   header = T)
# Data conversion
lpi$Entity=as.factor(lpi$Entity)

The study of biodiversity is a valuable indicator for understanding the ecosystem’s state of health and evolution.

Nowadays, a region’s diversity can be measured in various ways, trough tools and metrics like Red List Index of threatened species, the Biodiversity Intactness Index or the Living Planet Index.

What is the living planet index?

The Living Planet Index (LPI) is an indicator of biodiversity at both regional and global levels. It tracks the relative abundance of vertebrate populations across different biomes and continents over the past 55 years.

History of LPI

The LPI was originally conceived in 1997 by the World Wildlife Fund for Nature. This index has been used for international policy since 2006, but it is continuously being updated to provide a complete and accurate picture of trends in relative abundance.

How to calculate it

LPI at time t is given by

\[ LPI_t = e^{\frac{1}{S} \sum_{s=1}^{S} \ln R_{s,t}} \] Where:

  • S = Total number of species
  • R_s,t = Population change ratio for species s at time t. To calculate this ratio:

\[ R_{s,t} = \frac{N_{s,t}}{N_{s,t-1}} \]

Where, N_s,t = Population size of species s at time t

The Living Planet Index aggregates thousands of population time series from monitored species.

Show the code 
lpi_dt <- as.data.table(lpi)
lpi_world <- lpi_dt %>%
  filter(Entity == "World", Year %% 5 == 0, Year >= 1970, Year <= 2020) %>%
  select(Year, lpi_final)
print(as.data.frame(lpi_world), row.names = FALSE)
 Year lpi_final
 1970 100.00000
 1975  92.65501
 1980  78.43339
 1985  67.67479
 1990  60.09213
 1995  51.14914
 2000  44.37349
 2005  38.86755
 2010  31.09998
 2015  28.56733
 2020  27.13407
Show the code 
world_lpi <- lpi_dt %>%
  filter(Entity == "World")

plot <- ggplot(world_lpi, aes(x=Year, y=lpi_final)) + 
  geom_line(size=1.5) + 
  labs(x = "Year", 
       y = "Living planet index", 
       title = "Living planet index per year (interactive graph)") +
  expand_limits(y=0) +
  theme_classic()
ggplotly(plot)

Having an LPI of 50 implies that the populations have declined by 50% on average since 1970.

Show the code 
average_lpi = c()
decades = c("1970s", "1980s", "1990s", "2000s", "2010s", "2020s")
x = 1970

while(x < 2024) {
  avg_value <- lpi_dt[between(Year, x, x + 9), 
                      mean(lpi_final)] 
  average_lpi <- append(average_lpi, avg_value)
  x = x + 10
}

average_lpi_df <- data.frame(Decade = decades, Average_LPI = average_lpi)
kable(average_lpi_df, 
      col.names = c("Decade", "Average LPI"), 
      caption = "Average LPI per Decade")
Average LPI per Decade
Decade Average LPI
1970s 93.50212
1980s 76.89099
1990s 62.06579
2000s 47.25355
2010s 37.45647
2020s 33.79078

At a global level, since 1970, LPI has decreased progressively but alarmingly.

On average, vertebrate populations have been reduced by over 60%. But, even though a generalized decline has been observed, it varies by region and species group.

LPI regions

Average lpi by continent

Show the code 
lpi_table <- lpi_dt %>%
  filter(Year %in% c(1970, 1995, 2020), 
         Entity  != "World", Entity != "Freshwater") %>%
  select(Entity, Year, lpi_final) %>%
  pivot_wider(
    names_from = Year,
    values_from = lpi_final
  )

kable(lpi_table)
Entity 1970 1995 2020
Africa 100 44.05984 23.967030
Asia and Pacific 100 72.77707 39.603937
Europe and Central Asia 100 111.52333 64.715004
Latin America and the Caribbean 100 25.84670 5.377315
North America 100 81.87896 60.955050

Average lpi by continent

Show the code 
regions_lpi <- lpi_dt %>%
  filter(Entity != "World")

plot2 <- ggplot(regions_lpi, aes(x=Year, y=lpi_final, color=Entity)) + 
  geom_line() + 
  geom_point() +
  labs(x = "Year", 
       y = "Living planet index", 
       title = "Living planet index per year (interactive graph)") +
  scale_color_manual(values = c(
    "Africa"="darkgoldenrod3",
    "Asia and Pacific"="darkmagenta", 
    "Europe and Central Asia" = "deepskyblue2",
    "Freshwater"="turquoise",
    "Latin America and the Caribbean" = "darkkhaki", 
    "North America" = "chartreuse3")) +
  theme_classic()
ggplotly(plot2)

Among the different regions, these three stand out.

Show the code 
selected_lpi <- lpi_dt %>%
  filter(Entity %in% c(
    "Europe and Central Asia", 
    "Latin America and the Caribbean", 
    "North America"))

ggplot(selected_lpi, aes(x=Year, y=lpi_final, color=Entity)) + 
  geom_line(size=1.25) +
  scale_color_manual(values = c(
    "Europe and Central Asia" = "deepskyblue2",
    "Latin America and the Caribbean" = "darkkhaki", 
    "North America" = "chartreuse3")) + 
  labs(x = "Year", 
       y = "Living planet index", 
       title = "Living planet index per year") +
  facet_wrap(~ Entity) +
  theme_bw(base_size = 18)

On the one hand, the Latin America and Caribbean region has experienced the highest lost of biodiversity, while in North America there has been a noticeably milder and more regular decline. Finally, Europe presents and odd phenomenon, since biodiversity increased during the first decades of the study.

Why is that?

Biodiversity increase in Europe and Central Asia between 1970 and 1990 can be explained by a combination of historical, environmental, and policy-driven factors.

Show the code 
europe_lpi <- lpi_dt %>%
  filter(Entity == "Europe and Central Asia", Year <= 1990 )

ggplot(europe_lpi, aes(x=Year, y=lpi_final, color=Entity)) + 
  geom_line(size=1.25) +
  scale_color_manual(values = c("Europe and Central Asia" = "deepskyblue2")) + 
  labs(x = "Year", 
       y = "Living planet index", 
       title = "Europe betwen 1970 and 1990") +
  facet_wrap(~ Entity) +
  theme_bw(base_size = 18) +
  theme(legend.position = "none")

  1. After World War II, industrial decline led to lower pollution levels and, consequently, natural recovery of ecosystems.

  2. The European Union implemented strict environmental laws in the 1970s and 1980s that contributed to habitat restoration (e.g. 1979 Birds Directive).

Show the code 
year_lpi <- lpi_dt %>%
  filter(Year > 2010, Entity != "World", Entity != "Freshwater") 

ggplot(year_lpi, aes(Entity, lpi_final)) + 
  geom_boxplot() +
  scale_y_log10() + 
  labs(x = "Entity", 
       y = "Living planet index", 
       title = "Living Planet Index distribution after 2010")

The significant difference can be better seen in a boxplot, where Latin America and the Caribbean’s interquartile range (IQR) is much lower than the rest.

Implications of LPI Trends

Even though biodiversity loss is not uniform, downward trends in LPI are observed all around the globe. This indicates that species populations are shrinking, often due to human activity:

  • Habitat destruction.
  • Climate change.
  • Overexploitation of resources.
  • Pollution.

The future of LPI

Show the code 
recent_lpi <- lpi_dt %>%
  filter(Year >= 2020, Entity != "World", Entity != "Freshwater") %>%
  group_by(Entity) %>%
  summarize(avg_lpi = mean(lpi_final, na.rm = TRUE))

ggplot(recent_lpi, aes(x = reorder(Entity, -avg_lpi), y = avg_lpi, fill = Entity)) + 
  geom_bar(stat = "identity", show.legend = FALSE) +
  labs(x = "Region", 
       y = "Average Living Planet Index (Last 5 Years)", 
       title = "Average LPI by Region (Last 5 Years)") +
  expand_limits(y=100) + 
    scale_fill_manual(values = c(
    "Africa"="darkgoldenrod3",
    "Asia and Pacific"="darkmagenta", 
    "Europe and Central Asia" = "deepskyblue2",
    "Freshwater"="turquoise",
    "Latin America and the Caribbean" = "darkkhaki", 
    "North America" = "chartreuse3")) +
  theme_bw()

In recent years, the lowest levels of biodiversity ever seen have been recorded.

If the LPI continues to drop significantly, we might be in the verge of facing a global environmental crisis. Potentially ecosystems won’t be able to sustain themselves, leading to their collapse.

What Can Be Done?

Efforts at global and regional levels must be made to ensure stabilization of biodiversity. Some strategies entail:

  • Creating protected areas and wildlife corridors and expanding existing ones.
  • Promoting laws towards sustainable resource management.
    • Reducing deforestation.
    • Prohibiting habitat destruction.
    • Promoting better waste management strategies.
  • Tackling climate change.

Conclusion

The Living Planet Index is one of the most widely used metrics for biodiversity analysis. It is the study of vertebrate populations, which can be extrapolated to understand the health of ecosystems globally.

The gathered data over the last 50 years provides a picture of how these trends have progressed and in which direction they are heading. Thus, we see a general decline in biodiversity, which is a cause for concern and requires urgent conservation efforts.

Recent LPI Trends
Decade Average_LPI
2000s 47.25355
2010s 37.45647
2020s 33.79078

Bibliography

  1. Our World in Data. (n.d.). Global Living Planet Index. From https://ourworldindata.org/grapher/global-living-planet-index

  2. WWF International. (2022). Living Planet Report 2022. From https://wwfint.awsassets.panda.org/downloads/embargo_13_10_2022_lpr_2022_full_report_single_page_1.pdf

  3. WWF International. (1998). Living Planet Report 1998. From https://wwfint.awsassets.panda.org/downloads/livingplanetreport98.pdf

  4. Ledger, S. E. H., Loh, J., Almond, R., Böhm, M., Clements, C. F., Currie, J., Deinet, S., Galewski, T., Grooten, M., Jenkins, M., Marconi, V., Painter, B., Scott-Gatty, K., Young, L., Hoffmann, M., Freeman, R., & McRae, L. (2023). Past, present, and future of the Living Planet Index. NPJ Biodiversity, 2(1), 12. https://doi.org/10.1038/s44185-023-00017-3

  5. Loh, J., Green, R. E., Ricketts, T., Lamoreux, J., Jenkins, M., Kapos, V., & Randers, J. (2005). The Living Planet Index: Using species population time series to track trends in biodiversity. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1454), 289-95. https://doi.org/10.1098/rstb.2004.1584

  6. Jaureguiberry, P., Titeux, N., Wiemers, M., Bowler, D. E., Coscieme, L., Golden, A. S., Guerra, C. A., Jacob, U., Takahashi, Y., Settele, J., Díaz, S., Molnár, Z., & Purvis, A. (2022). The direct drivers of recent global anthropogenic biodiversity loss. Science Advances, 8(45), eabm9982. https://doi.org/10.1126/sciadv.abm9982

Dataset (Interactive data table)

Show the code 
datatable(
  data = lpi_dt, 
  options = list(
    pageLength = 10,    
    scrollX = TRUE,     
    autoWidth = TRUE,   
    searchHighlight = TRUE 
  ), 
  class = "cell-border stripe", 
  editable = "cell",   
  width = "1200px",  
  height = "100px" 
)