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Introduction

Conservation finance is an important field in the KfW development bank with considerable investment into Protected Areas (Short PAs). The evaluation department, together with operational departments tries to learn more about our conservation projects by using our project documentation as well as open geo-datasets to assess the relevance and effectiveness of supported areas in Latin America. The main impact goals of our conservation financing efforts can be summarized under three broad topics:

  1. Conservation of biological diversity
  2. Mitigation of global climate change e.g by reducing deforestation
  3. Improvement of livelihoods of the local population that uses the natural resources

Conservation finance has increased in importance for German development cooperation and considerably more financial resources had been spent in Latin America since 2004.

We machted our Latin America portfolio with the World Database on Protected Areas - WDPA (IUCN) and used data from the multiple different open data-sources to make an assessment of our portfolio and evaluate the impacts of our projects. Our database currently comprises 398 PAs in Latin America which are situated in 15 different countries. Those areas can be broadly categorized into 337 terrestrial, 19 marine, and 42 partial marine/terrestrial protected areas. They cover a total surface of 0.989 Mio. km2 which is about 2.8 times the size of Germany.

Ecoregions and Biomes

Conservation planning needs careful consideration on how to allocate limited financial resources in order to preserve the most relevant, remaining natural landscapes. In order to put money to work where it is most urgently needed, targeted ares should be high in biodiversity and endemism, as well as ecosystem functionality (and the degree to which they are threatened). Therefore, we need to use an adequate conceptual model that characterizes different natural areas in terms of biodiversity and ecosystem functionality. This will allow us to see how our financial resources are allocated among distinctive biotas and if we have an overall bias towards specific ecosystems while other relevant areas slip our attention.

To that end we utilize the Terrestrial Ecoregions of the World (TEOW) classification system from Olson et al..1 The TEOW system is based on biogeographic knowledge from thousands of experts and designed with the purpose of conservation planning in mind. It differentiates between 867 ecoregions which are nested in 14 large biomes. Ecoregions are defined as relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change (Olson et al.). Each ecoregion is part of a terrestrial biome which is a major ecosystem division that shares a common climate, vegetation type (e.g. tundra or forest), soiltype and wildlife.

KfW supports PAs that cover 100 different ecosystems and 10 biomes. This corresponds to 65% of all terrestrial ecoregions in our 15 partnering countries. This relatively high share indicates a broad diversification of the investment portfolio. Nevertheless we should compare total area size of supported ecoregions to get a more adequate representation of the utilized funds. The graphic below allows us to see the actual numbers for each ecoregion grouped by larger biomes.

We can clearly see that our support is heavily concentrated in tropical and subtropical moist broadleaf forest areas. This biome alone makes up 84% of the total supported areas. Most of these areas are concentrated in the Amazon Basin. Nevertheless, we see that within this huge biome very different ecoregions are supported to differing degrees as well.

Note: It seems here that e.g. very few mountaineous regions are supported, relatively few mangrove areas and few Savannas. It would be interesting if there are any global priorities discussed currently in terms of biomes or ecoregions.

Ecosystem Services

Carbon Storage

Geodata can also tell us a bit about the relevance of conserving forests to mitigate global climate change. The following chart shows us, for example, estimations on how much carbon is stored in the soils as well as in the belowground and aboveground biomass inside supported protected areas. Conservation finance can help to keep this carbon in place that might be otherwise released to the atmosphere due to deforestation and forest degradation.

As we can see the protected areas network in Latin America stores 18.03 Gigatons of carbon that, if released completely to the atmosphere, would generate emissions which correspond to 90 times the annual GHG emissions of Germany. Most of this carbon is stored in Brazil which is by far the largest country on the continent with the biggest network of protected areas.

Habitat and Species

PAs supported through KfW cover the habitat of several species that are threatened with extinction according to the IUCN Red List of Threatened Species.

Threads and Landcover Change

Forestcover loss and area usage rights can be correlated. In the following we will assess whether there is an apparent association between forestcover loss and different usage categories according to the usage criteria from IUCN. The plots show the trends before and after project start (horizontal lines). Shown are relative losses compared to the baseline year 2000. I.e. the total forestcover area that was still available in 2000 corresponds to 100% in the plots.

IUCN Categories are as follows:

  • Ia Strict Nature Reserve: Category Ia are strictly protected areas set aside to protect biodiversity and also possibly geological/geomorphical features, where human visitation, use and impacts are strictly controlled and limited to ensure protection of the conservation values. Such protected areas can serve as indispensable reference areas for scientific research and monitoring.

  • Ib Wilderness Area: Category Ib protected areas are usually large unmodified or slightly modified areas, retaining their natural character and influence without permanent or significant human habitation, which are protected and managed so as to preserve their natural condition.

  • II National Park: Category II protected areas are large natural or near natural areas set aside to protect large-scale ecological processes, along with the complement of species and ecosystems characteristic of the area, which also provide a foundation for environmentally and culturally compatible, spiritual, scientific, educational, recreational, and visitor opportunities.

  • III Natural Monument or Feature: Category III protected areas are set aside to protect a specific natural monument, which can be a landform, sea mount, submarine cavern, geological feature such as a cave or even a living feature such as an ancient grove. They are generally quite small protected areas and often have high visitor value.

  • IV Habitat/Species Management Area: Category IV protected areas aim to protect particular species or habitats and management reflects this priority. Many Category IV protected areas will need regular, active interventions to address the requirements of particular species or to maintain habitats, but this is not a requirement of the category.

  • V Protected Landscape/ Seascape: A protected area where the interaction of people and nature over time has produced an area of distinct character with significant, ecological, biological, cultural and scenic value: and where safeguarding the integrity of this interaction is vital to protecting and sustaining the area and its associated nature conservation and other values.

  • VI Protected area with sustainable use of natural resources: Category VI protected areas conserve ecosystems and habitats together with associated cultural values and traditional natural resource management systems. They are generally large, with most of the area in a natural condition, where a proportion is under sustainable natural resource management and where low-level non-industrial use of natural resources compatible with nature conservation is seen as one of the main aims of the area.

Appendix


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] htmltools_0.5.1.1  ggthemes_4.2.4     plotly_4.9.3       RColorBrewer_1.1-2
 [5] leaflet_2.0.4.1    sf_0.9-8           janitor_2.1.0      readxl_1.3.1      
 [9] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4       
[13] readr_1.4.0        tidyr_1.1.3        tibble_3.1.1       ggplot2_3.3.4     
[17] tidyverse_1.3.1   

loaded via a namespace (and not attached):
 [1] httr_1.4.2              viridisLite_0.4.0       jsonlite_1.7.2         
 [4] modelr_0.1.8            assertthat_0.2.1        cellranger_1.1.0       
 [7] yaml_2.2.1              pillar_1.6.0            backports_1.2.1        
[10] glue_1.4.2              digest_0.6.27           promises_1.2.0.1       
[13] rvest_1.0.0             snakecase_0.11.0        leaflet.providers_1.9.0
[16] colorspace_2.0-1        httpuv_1.6.1            pkgconfig_2.0.3        
[19] broom_0.7.6             haven_2.3.1             scales_1.1.1           
[22] whisker_0.4             later_1.2.0             git2r_0.28.0           
[25] proxy_0.4-26            farver_2.1.0            generics_0.1.0         
[28] ellipsis_0.3.2          withr_2.4.2             lazyeval_0.2.2         
[31] cli_2.5.0               magrittr_2.0.1          crayon_1.4.1           
[34] evaluate_0.14           fs_1.5.0                fansi_0.5.0            
[37] lwgeom_0.2-6            xml2_1.3.2              class_7.3-19           
[40] tools_3.6.3             data.table_1.13.6       hms_1.0.0              
[43] lifecycle_1.0.0         munsell_0.5.0           reprex_2.0.0           
[46] compiler_3.6.3          e1071_1.7-7             rlang_0.4.11           
[49] classInt_0.4-3          units_0.7-1             grid_3.6.3             
[52] rstudioapi_0.13         htmlwidgets_1.5.3       crosstalk_1.1.1        
[55] labeling_0.4.2          rmarkdown_2.6           gtable_0.3.0           
[58] DBI_1.1.1               R6_2.5.0                lubridate_1.7.10       
[61] knitr_1.30              utf8_1.2.1              workflowr_1.6.2        
[64] rprojroot_2.0.2         KernSmooth_2.23-20      stringi_1.6.2          
[67] Rcpp_1.0.6              vctrs_0.3.8             dbplyr_2.1.1           
[70] tidyselect_1.1.1        xfun_0.20

  1. REF - Olson et al: https://academic.oup.com/bioscience/article/51/11/933/227116↩