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The following analysis is intended to support a peer-discussion on the threat level of protected areas (short: PAs) in the Selva Maya region located in Mexico, Belize and Guatemala. The goal of this exercise is to assist KfW and its partners in the project preparation phase. In addition, information about the current portfolio and its relevance to protect the most threatened areas can be derived.
We look at a set of predefined PAs, rank it according to the their threat level and compare it to the past KfW portfolio. The analysis is based on publicly available data from the World Database of Protected Areas (WDPA/IUCN) 1 and other freely available geodata sources. In a first analysis step we focus on habitat destruction in terms of primary forest cover loss and in the second step we look at fires in protected areas between 2000 and 2021. We also show a selection of landcover statistics and population numbers. These datasets can indicate human pressure on the ecosystem as well as natural/climatic stressors that could harm the long-term stability and provision of ecosystem services.
Below you can find an overview of the analyzed areas. Data was downloaded from the WDPA. Some of the analyzed areas appear to be overlapping and some also show two entries in the WDPA. The following data gives some insights into governance variables as provided by the WDPA.
To quantify forest cover loss we utilize data from the Global Forest Watch (Hansen et al, 2013)2. Forest cover loss is defined in their methdology “as a stand-replacement disturbance, or a change from a forest to non-forest state.”. Loss can either be the result of human activities or natural factors such as droughts, forest fires or hurricanes, amongst others. More information on the interpretation and usefulness of this data as well as suggested further steps to advance the threat assessment are given below in the discussion part.
In the analysis we will focus on two key outcome indicators:
Total forest cover loss: Measures the total sum of loss areas in hectare. This variable is able to identify PAs with the highest primary forest cover loss between 2001 and 2020. The identification of high loss areas can be useful for targeting areas where we might achieve the largest impact in terms of reducing emissions from deforstation and forest degradation.
Relative forest cover loss: Measures the percentage of primary forest cover loss inside a PA compared to its total primary forest area in 2000. The identification of PAs with high relative losses can be relevant from a biodiversity perspective. PAs with high relative losses might be places where large parts of the functional forest habitat is lost. Targeting these areas might not only help to protect the floral biodiversity but also the fauna and humans that inhabit these areas and profit from the local forest ecosystem services.
The following map depicts relative and absolute forest cover loss in the selected areas. The size of circles is dependent on the total loss (the bigger the total loss, the larger the circle). The color is dependent on the relative loss (red circles indicate areas with high loss). From a threat perspective big red circles could be especially relevant areas for conservation.
This map is interactive, meaning that you can zoom into the map to see specific countries in more detail and click on areas to get summary statistics. Furthermore, supported PAs from the current and past portfolio of KfW (blue), including a 10 km buffer zone (red), are displayed with their actual polygon boundaries as well as all other PAs in the surrounding area (grey) which can be activated manually in the map. Furthermore the analyzed data from Global Forest watch can be seen when activated in the layer control panel as well as the distribution of primary forests in 2001.
For analysis purposes it is useful to also activate the satellite layer which can often indicate whether areas are affected by agricultural conversion.
The following “Lollipop Plots” are be used to quickly visualize the total area affected by forest cover loss and sort protected areas from low to high regarding the total area affected. The figure furthermor shows if any of these PAs has been or is currently supported by a project from KfW. You can get detailed statistics by hovering over the lollipops with your mouse.
Heatmaps are used to assess the time-trend of one or several PAs. We use heatmaps instead of lineplots in order to increase readibility of the plot. The heatmap exhibits variation in the total area that was affected by forest cover loss on an annual base. Green colors suggest a low amount of loss and red colors indicate a larger loss area.
You can get more detailed information by hovering with the mouse over the the individual cells. It is important to mention that the analysis does not allow to differentiate between natural and human induced losses. Therefore a more profound analysis with the map can be helpful. Sometimes outstanding large forest cover loss events can indicate natural losses, whereas continuous loss events over several years might indicate ongoing anthropogenic conversion of areas for agriculture and livestock farming.
This section covers the quantification and analysis of forest fires between 2000-2021, which are taken from the NASA FIRMS archives 3. We sum up fires on an annual base for all protected areas. For simplicity we leave out fire intensity and burned area in this analysis. For caveats on this approach please see the Interpretation and Discussion section below.
The following map depicts a summary of fire counts in the suggested Protected Areas according to NASA Firms. In general larger protected areas experience more fires due to their spatial extension. Nevertheless there are also exceptions.
There is also a notable correlation between forest cover loss and forest fires (You can activate the forest cover loss layer in the map). This seems plausible since fires are often used as a strategy for forest cleansing or large natural wildfires might cause permanent damage to the forest cover. However, the relationship is not perfect.
The following “Lollipop Plots” are be used to quickly visualize the total number of fires and sort protected areas from low to high regarding the total number of fire occurrences. The figure furthermore shows if any of these PAs has been or is currently supported by a project from KfW. You can get detailed statistics by hovering over the lollipops with your mouse.
Heatmaps are used again to assess the time-trend of one or several PAs. They show the variation in the total number of fires which can be attributed to human activities or natural factors such as El Niño and La Niña (For more information see here).
Blue color suggest a low number of fire whereas red colors indicate a larger number. Missing values are white. You can get more detailed information by hovering with the mouse over the the individual cells.
The density plots below give an idea of the fire seasonality over all observed years. The prominent peaks from march to may indicate that nearly all fire activity occurs during those months, which coincides with the local dry season.
The plots above show the number of detected fires per time unit. Additionally, the FIRMS data also contain information about the fire radiative power (FRP) in gigawatts [GW]. But because of the high correlation between fire frequency and FRP (Spearman’s \(\rho = 0.91\)), we only show one of them as plots.
This plot shows the ESA land cover data for the year 2019 in and around the PAs. By double clicking on a class in the legend, you can single out categories for further examination. The second plot shows the same statistics, but for the buffer region around the PAs rather than the actual PAs.
The population count layer for years 2000 – 2020 is published by the open spatial demographic data and research organization WorldPop 4. Depending on the area, different population estimation methods are used. The data should therefore be interpreted as an indicator for population dynamics in and around the PAs rather than an exhaustive population count.
As outline above the GFW methodology defines forest cover loss as a “as a stand-replacement disturbance, or a change from a forest to non-forest state.”. Loss can either be the result of human activities or natural factors such as droughts, forest fires or hurricanes, amongst others. Thus, the data currently does not allow the differentiate permanent loss and conversion from temporary loss due to natural factors. A more in-depth analysis of pre-selected areas is therefore recommended when using the current data.
GFW data can be used to get an assessment of old-growth and primary forests as well as associated carbon emissions. It can indicate which areas had been highly threatened either due to natural causes (storms/fires) or human causes (deforestation/logging/degradation and conversion to agriculture/silviculture). Especially useful in this context is to have a look at the whole trend from 2001 to 2020. Huge spikes in the individual trend data might indicate natural causes such as fires and tropical storms A quick search with Google always helps to confirm this hypothesis.
More continuous growth in forest loss is probably due to the conversion of natural forests for agricultural purposes. Again you might look at the map given above and activate the satellite layer to see what might be happening below the detected loss. GFW does not (currently) allow to detect regrowth and regeneration but it is planned to provide that feature soon.
Possible Improvements
The utilized indicator (sum of forest fires) neglects the fact that fires exhibit a different intensity and that the area affected by fires differs amongst observations. Nevertheless, area estimates can be derived from the forest cover loss data as well.
Also the utilized indicator does not differentiate between biomes. Fires in dry regions and Savannas might be more common and have a vital function for the ecosystem, whereas they might be more problematic in moist rainforest where the local vegetation is less able to cope with fire stress.
Possible Improvements
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
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[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: Europe/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] plotly_4.10.2 RColorBrewer_1.1-3 htmltools_0.5.5
[4] scales_1.2.1 ggsci_3.0.0 leaflet.extras2_1.2.1
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[10] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0
[13] dplyr_1.1.2 purrr_1.0.1 readr_2.1.4
[16] tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.2
[19] tidyverse_2.0.0
loaded via a namespace (and not attached):
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[4] htmlwidgets_1.6.2 tzdb_0.4.0 leaflet.providers_1.9.0
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[10] generics_0.1.3 parallel_4.3.1 proxy_0.4-27
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[40] labeling_0.4.2 rprojroot_2.0.3 fastmap_1.1.1
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[46] magrittr_2.0.3 utf8_1.2.3 e1071_1.7-13
[49] withr_2.5.0 promises_1.2.0.1 bit64_4.0.5
[52] timechange_0.2.0 httr_1.4.6 rmarkdown_2.23
[55] bit_4.0.5 workflowr_1.7.0 hms_1.1.3
[58] evaluate_0.21 knitr_1.43 viridisLite_0.4.2
[61] markdown_1.7 s2_1.1.4 rlang_1.1.1
[64] Rcpp_1.0.11 glue_1.6.2 DBI_1.1.3
[67] vroom_1.6.3 rstudioapi_0.15.0 jsonlite_1.8.7
[70] R6_2.5.1 fs_1.6.2 units_0.8-2 UNEP-WCMC and IUCN (2022), Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) [Online], February 2022, Cambridge, UK: UNEP-WCMC and IUCN. Available at: www.protectedplanet.net.↩︎
“Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.”↩︎
“Justice, C.O., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, J., Alleaume, S., Petitcolin, F., & Kaufman, Y.J. (2002). The MODIS fire products. Remote Sensing of Environment, 83:244-262. doi:10.1016/S0034-4257(02)00076-7”↩︎
www.worldpop.org↩︎