Introduction

In this project, we are focusing on the Indicator 15.4.1 “coverage by protected areas of important sites for mountain biodiversity”. Protected areas, as defined by the International Union for Conservation of Nature are defined as follows: “geographical spaces, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values”.

For the analysis of this indicator, we have chosen the Southern American region due to its important biodiversity. The region counts with important mountain areas, rivers and rainforests such as the Amazon, which is included in the list of World Heritage sites. However, not all Southern American countries are equally mountainous: while The Andes Mountains crosses countries like Argentina and Chile, others like Uruguay are quite flat.

The indicator is, naturally, linked to target 15.4, which searches to ensure by 2030 the conservation of mountain ecosystems. Before continuing with the analysis, let’s explain why mountains are so important, and then, what reason justifies taking a closer look at mountain biodiversity in South America.

Mountains are essential to life because they provide between 60 and 80 percent of all freshwater resources for our planet. Moreover, they are important centers of agricultural resources and also are home to a great variety of fauna and flora (Ecowatch, 2021)

According to (Chaverri-Polini, 2022), the chemical characteristics of the mountain soils of Central and South America, along with mild and low soil and atmospheric temperatures are elements that limit plant growth. This has caused that, in order to survive, plants and associated wildlife have had to adapt to their surrounding conditions in order to survive. As a result, the mountain’s fauna and flora have a high level of adaptability, which allows them to establish and reproduce even if the conditions for it are not optimal. This explains the richness and diversity found in the mountains, and the reason why these environments, specially in South America, have to be protected.

This is further emphasized by the UN (2021) as it highlights that the safeguard of important sites is vital for stemming the decline in biodiversity and ensuring long term and sustainable use of mountain natural resources.

Current trends suggest that ecosystems in the region are degrading, with the subsequent damage that this causes in the environment. As such, we wanted to study to what extent Latin American governments are adressing this issue and increasing the range of protected areas.

The data set that that we used was originally named “ER_PTD_MTN” and was provided by the UN.

data =sdg_data("ER_PTD_MTN")

Within it, the main variable for our study was that one of “value”, which referred to the percentage of mountainous area protected in each country and for each year. For that reason, the variables of “GeoAreaName” and “TimePeriod” were also important.

This indicator serves as a means of measuring progress toward the conservation, restoration and sustainable use of mountain ecosystems and their services, in line with obligations under international agreements and conventions such as the “Strategic Plan for Biodiversity” and the “Global Biodiversity Framework”, both instruments convened under the umbrella the UN Convention on Biological Biodiversity (ratified by all South American countries).

Data Manipulation

Even though we mainly relied on the previously mentioned variable “Data”, it did not include any variable that would allow us to only select South American countries. Therefore, we used another database from the World Bank (“codelist”) to import that variable.

data("codelist")

After inputting the new database and before undertaking the analysis we had to select the data corresponding to the countries of interest for this research (i.e. countries of South America).

Consequently, we proceeded to merge both databases using the left_join function, through the “GeoAreaCode” variable, which was the common one to both databases.

data = data %>% 
  left_join(codelist %>% rename(GeoAreaCode=iso3n),by="GeoAreaCode")

To distinguish it from the previous one, we changed the name of the new database to that of “LAM” (making reference to the region under study) including the variables from both data sets. After doing so, we were able to select the cases corresponding to South American countries using the filter function with the “region23” variable and the label “South America”.

lam=data %>% filter(region23=="South America")

After eliminating different variables that would not help in the study, our database was ready for the analysis.

Analysis

The first thing that we wanted to analyze was the evolution of the protected areas in South American countries between 2010 and 2020. To do so, we first created a points plot using the “ggplot” function, and establishing the the “TimePeriod” variable in the “X” axis and “value” in the “y” one, In addition, we established blue as the color to represent the variations, resulting in the following graph:

lam %>% 
   ggplot (aes(x=TimePeriod,y=GeoAreaName,color=Value))+ geom_point()

Even though it provided some interesting preliminary results, such as the abrupt increase in the percentage of mountainous land protected in French Guyana and Paraguay in [insert year], they were no so clear for other countries as a result of the typology of the plot. Consequently, and after converting it in an object labelled “g2”, we used the same function to create another plot in which data was displayed in lines. Also, we displayed the function “direct.label” to understand it more clearly resulting in the following graph:

g2= lam %>% 
    ggplot (aes(x=TimePeriod,y=Value,color=GeoAreaName))+ geom_line() + theme_bw()

direct.label(g2)

This new plot uncovered trends that were not reflected in the previous one. More precisely, it showed that except Venezuela and Uruguay, the rest of the countries have progressively increased the percentage land areas of this type that are protected in the region.

It seemed that countries such as Brazil or Colombia did not make much progress. However, and as previously mentioned, not all South American countries have not the same percentage of their territory covered by mountains areas. Consequently, progress should be weighted taking into consideration that aspect.

For doing so, and using the “pivot” function, we created the following plot:

wide=lam %>% select(starts_with("Geo"),TimePeriod,Value) %>%  
  pivot_wider(names_from=TimePeriod,values_from=Value) %>% 
  mutate(Change=(`2021`-`2000`)/`2000`*100)

wide %>% arrange(`2021`) %>% ggplot(aes(y=GeoAreaName,yend=GeoAreaName,x=`2000`,xend=`2021`)) + geom_segment() +
  geom_point(aes(x=`2000`,color="2000")) + geom_point(aes(x=`2021`,color="2021"))

As a result, we can see that countries such as Colombia have made much more efforts in protecting these areas that countries like Paraguay, in spite of what the first graphs tended to conclude.

Explanation

If we take a look at the pattern of conservation management in the SDG tracker, we find that since 2000, Bolivia, Venezuela, Suriname and French Guayana were the areas in which the conservationist efforts were found the most all over the South American region. The will towards conservation of biodiversity (to which we include mountain biodiversity) developed through the following years with the incorporation of Peru and Ecuador to conservation efforts. In the year 2007, French Guayana reached a peak with the outstanding percentage of 80,7% of protected biodiversity areas. By 2021, we can conclude that Brazil, Bolivia and Colombia have linearly developed their conservation strategies over time, whereas the western countries like Ecuador, Peru and Chile have remained stuck. However, we can better appreciate the results in the cases of Venezuela and French Guarana since both countries are at the top of the ranking regarding biodiversity conservation, with the consequent results of French Guayana: 83,15% and Venezuela 78,78.

Conclusions

The conservation of cross-border areas has been reflected in the increased international efforts that aim to protect the value of mountain ecosystems, augmenting through the years. These efforts are particularly directed towards the conservation of certain areas; to these measures we can add the establishment of biosphere reserves and the ecological corridors.

Specialists argue that conservation management strategies should be understood and adopted by the people as well as the will to promote biodiversity conservation should be a matter of attendance by the international community in the era of globalization.

Finally, environmental education plays a key aspect in view of increasing the awareness concerning the preservation of ecosystems with biological capacities.

According to Adelaida Chaverri, environmental sciences expert from the University of Costa Rica, Globalization should not only be economically driven; it should also include the conservation and sustainable management of vegetation in montane areas and other altitudinal zones throughout the world (Chaverri Polini, 2022).

Bibliography

Chaverri Polini, A. (2022). Mountains, biodiversity and conservation. Retrieved 24 May 2022, from https://www.fao.org/3/w9300e/w9300e09.htm EcoWatch. (2021, November 4). 7 reasons why #mountains matter. EcoWatch. Retrieved May 24, 2022, from https://www.ecowatch.com/international-mountain-day-2018-2623054039.html Goal 15: Life on Land - SDG Tracker. Retrieved 24 May 2022, from https://sdg-tracker.org/biodiversity UN, (2021). International Convention on Biological Diversity. Retrieved 24 May 2022, from https://www.cbd.int/information/parties.shtml