Last updated: 2021-03-16

Checks: 6 1

Knit directory: mapme.protectedareas/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210305)

The command set.seed(20210305) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 45f3a67

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 45f3a67. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    contributing.html
    Ignored:    mapme.protectedareas.Rproj
    Ignored:    renv/library/
    Ignored:    renv/local/
    Ignored:    renv/staging/

Unstaged changes:
    Modified:   .DS_Store
    Modified:   analysis/index.Rmd
    Modified:   analysis/wwf_teow.rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/wwf_teow.rmd) and HTML (docs/wwf_teow.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 45f3a67 Ohm-Np 2021-03-16 add biomes name column & slight modifications
html 45f3a67 Ohm-Np 2021-03-16 add biomes name column & slight modifications
Rmd 0a7f686 Johannes Schielein 2021-03-15 updated teow workflow
html 0a7f686 Johannes Schielein 2021-03-15 updated teow workflow
Rmd c93be45 Johannes Schielein 2021-03-15 update to teow routine
Rmd b548133 Ohm-Np 2021-03-15 create wwf teow rmd
html b548133 Ohm-Np 2021-03-15 create wwf teow rmd 
# load required libraries
library("sf")
library("wdpar")
library("dplyr")
library("mapview") # usef for interactive maps in this website. not essential for analysis
library("rmarkdown") # only used for rendering tables  for this website

starttime<-Sys.time() # mark the starttime of this routine to calculate processing time at the end

Introduction

Terrestrial Ecoregions of the World (TEOW) is a biogeographic regionalization of the Earth’s terrestrial biodiversity. The biogeographic units are ecoregions, which are defined as relatively large units of land or inland water containing a distinct assemblage of natural communities sharing a large majority of species, dynamics, and environmental conditions. There are 867 terrestrial ecoregions, classified into 14 different biomes such as forests, grasslands, or deserts. Ecoregions represent the original distribution of distinct assemblages of species and communities.Visit Link for more information on TEOW from WWF.

Datasource and Metadata Information

  • Dataset: Terrestrial Ecoregions of the World - World Wildlife Fund [1]
  • Geographical Coverage: Global
  • Temporal Coverage: 2001
  • Temporal Resolution: Cross-sectional
  • Unit: hectare
  • Data downloaded: 15th March, 2021
  • Metadata Link
  • Download Link

Here we are going to carry out an analysis that intersects WDPA polygons with the Ecoregions to calculate the area of different ecoregions and biomes within supported PAs.

Processing Workflow

To carry out this analysis, we will follow this processing routine:

Download and prepare WDPA polygons

First of all, we will try to get the country level polygon data from wdpar package. wdpar is a library to interface to the World Database on Protected Areas (WDPA). The library is used to monitor the performance of existing PAs and determine priority areas for the establishment of new PAs. We will use Brazil - for other countries of your choice, simply provide the country name or the ISO3 name e.g. Gy for Guyana, COL for Colombia

# fetch the raw data from wdpar of country
br_wdpa_raw <- wdpa_fetch("Brazil")

Since there are more than 3000 enlisted protected areas in Brazil, we will demonstrate this routine using only three wdpa polygons: - Reserva Biologica Do Rio Trombetas - wdpaid 43, - Reserva Extrativista Rio Cajari - wdpaid 31776, and - Estacao Ecologica Do Jari - wdpaid 4891

For this, we have to subset the country level polygon data to the PAs level.

# subset three wdpa polygons by their wdpa ids
br_wdpa_subset<-
  br_wdpa_raw%>%
  filter(WDPAID %in% c(43,4891,31776))

The next immediate step is to clean the fetched raw data with the functionality provided with routines from the wdpar package. Cleaning is done by the package following this steps:

  • exclude protected areas that are not yet implemented
  • exclude protected areas that are UNESCO reserves
  • removing points with no reported area:
  • replace missing data codes (e.g. “0”) with missing data values (i.e. NA)
  • replace protected areas represented as points with circular protected areas that correspond to their reported extent
  • repair any topological issues with the geometries
  • (erase overlapping areas) -> which might be important for area reporting on country level but is not done in our context here

For more information see the package documentation of wdpar. In the end of this step we will project the data back to WGS84 for the intersection.

# clean the data
br_wdpa_subset <- wdpa_clean(
  br_wdpa_subset, 
  erase_overlaps = F
  )
# we can create an interactive map to see the three selected polygons
mapView(br_wdpa_subset)

Since, we prepared WDPA polygon data for our analysis, we now load the TEOW global shapefile layer from archived file.

# load TEOW global polygons
teow <- 
  read_sf(paste("data/Terrestrial_Ecoregions_World.shp", sep=""))

Intersect TEOW and WDPA Polygon layer

To analyse how much of wdpa area is within which part of the ecoregion, intersection function is applied. st_intersection allows us to see that result. To be able to apply st_intersection, the polygon layers should be provided as an object with the sf class. To carry out intersection function, coordinate reference system of both the polygons should be harmonized. For this, we use st_transform to achieve this. We than compare the number of polygons in both the original and the resulting WDPA layer to see whether our process split up any polygon from the intersection

# reproject teow to match WDPA
teow <-
  st_transform(teow,crs = st_crs(br_wdpa_subset))
# apply intersection
teow_wdpa_intersection <- 
  st_intersection(teow, br_wdpa_subset)
# compare the number of polygons in both layers 
nrow(br_wdpa_subset)
[1] 3
nrow(teow_wdpa_intersection)
[1] 4

We can see that there is one intersection in the research area i.e that we now have four polygons whereas before the intersection we had only three.

Calculate Areas

Since, we already achieved the intersection, now we want to extract the actual area of intersection between wdpa polygons and teow polygons.

# extract areas (SqKm) and save it as new column
teow_wdpa_intersection$teow_intersect_sqkm <- 
  st_area(teow_wdpa_intersection)/1000000
# tibble - turns existing object to tibble dataframe from library `dplyr`
myData <- as_tibble(teow_wdpa_intersection)
# select only necessary columns from the intersected polygon
myData_f <- myData %>% 
  select(WDPAID, BIOME_NAME, ECO_NAME, teow_intersect_sqkm)

With the results looking like this

In the end we are going to have a look how long the rendering of this file took so that people get an idea about the processing speed of this routine.

stoptime<-Sys.time()
print(starttime-stoptime)
Time difference of -5.119396 secs

References

[1] Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D’Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] rmarkdown_2.5   mapview_2.9.0   dplyr_1.0.5     wdpar_1.0.6    
[5] sf_0.9-7        workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6              countrycode_1.2.0       lattice_0.20-41        
 [4] png_0.1-7               class_7.3-17            leaflet.providers_1.9.0
 [7] assertthat_0.2.1        rprojroot_2.0.2         digest_0.6.27          
[10] R6_2.5.0                stats4_4.0.3            evaluate_0.14          
[13] e1071_1.7-4             httr_1.4.2              pillar_1.4.7           
[16] rlang_0.4.10            curl_4.3                rstudioapi_0.13        
[19] whisker_0.4             raster_3.4-5            webshot_0.5.2          
[22] stringr_1.4.0           htmlwidgets_1.5.3       munsell_0.5.0          
[25] compiler_4.0.3          httpuv_1.5.4            xfun_0.19              
[28] pkgconfig_2.0.3         base64enc_0.1-3         htmltools_0.5.0        
[31] tidyselect_1.1.0        tibble_3.0.4            codetools_0.2-16       
[34] crayon_1.3.4            later_1.1.0.1           rappdirs_0.3.3         
[37] grid_4.0.3              jsonlite_1.7.1          lwgeom_0.2-5           
[40] satellite_1.0.2         lifecycle_1.0.0         DBI_1.1.0              
[43] git2r_0.27.1            magrittr_2.0.1          units_0.6-7            
[46] scales_1.1.1            KernSmooth_2.23-17      stringi_1.5.3          
[49] renv_0.13.0             fs_1.5.0                promises_1.1.1         
[52] leaflet_2.0.4.1         sp_1.4-5                ellipsis_0.3.1         
[55] generics_0.1.0          vctrs_0.3.6             RColorBrewer_1.1-2     
[58] tools_4.0.3             leafem_0.1.3            glue_1.4.2             
[61] purrr_0.3.4             crosstalk_1.1.1         yaml_2.2.1             
[64] colorspace_2.0-0        classInt_0.4-3          knitr_1.30