+ Locating KBAs: An automated workflow for +identifying potential Key Biodiversity Areas +
++ [Name Redacted] - National Audubon Society +
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- + + Introduction + + +
- + + Methodology + + + +
- + + Example outputs + + +
- + + How to run the workflow code in your local +computer + + +
+ Introduction +
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+ In the face of an alarming and accelerating loss of biodiversity, it +is essential to safeguard the places that contribute significantly to +the persistence of species around the world. The IUCN Species Survival +and World Protected Areas Commissions recently led an effort to develop +a + + standard + + for identifying the places with the highest conservation value, the Key +Biodiversity Areas (KBAs; IUCN, 2016). In the process of identifying +KBAs, it is recommended to first conduct a comprehensive scoping +analysis (KBA Standards and Appeals Committee, 2020). However, this +analysis requires a great effort to obtain, compile and analyze spatial +data for as many taxonomic groups as possible. The + + Locating KBAs +workflow + + helps to streamline the scoping analysis by using the R +programming language and leveraging GBIF data. The workflow code +accesses the occurrence data of species present in a user-defined area +and determines all those that could trigger the criteria of the KBA +standard. Subsequently, it helps users identifying the places where the +potential trigger species occur in significant numbers. In this way, the +workflow enables users to efficiently and transparently identify the +sites where it is most appropriate to focus efforts on gathering +information on mature individuals, engaging stakeholders, and applying +the standard’s criteria. +
+ ++ + On this website you can find an overview of the workflow and +the requirements and steps necessary to run the code. Click on the +Notebook tab to see the workflow code and interact with the main +results, such as tables and maps. Click on the GitHub repository tab to +go to the repository where the script is stored. You can clone the +script on your computer to modify and run it locally (see section + + How to run the workflow code in your local +computer + + ). + +
++ Methodology +
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+ 1. Installation and initial requirements +
++ To get started, you need to confirm that you have the following +requirements: +
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+ Have R and RStudio installed on your computer. If you do not have +them, please download and install + + R + + first and continue downloading +and installing + + RStudio + + . +
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+ Have a GitHub account and GitHub Desktop installed on your +computer. You can create a free GitHub account + + here + + and download GitHub Desktop + + here + + . +
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+ Have a GBIF account. If you do not have one yet, create it using +this + + link + + . +
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+ Request an API token + + here + + to access the IUCN Red +List information. +
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+ Once you have met all the above requirements, open RStudio and make +sure you have the following packages installed; otherwise use the +following code to install them: +
+install.packages(c("tidyverse", "Hmisc", "rgbif", "wellknown", "rredlist", "ggplot2", "ggspatial", "RCurl", “xml2”, “rvest”, “sf”, "sp", “DT”, “leaflet”, “knitr”, "foreach", "doSNOW", "parallel", "stringi", "CoordinateCleaner", "countrycode", "taxadb"))+ 2. Workflow +
++ The workflow code presented in the notebook tab, accessible through +the GitHub repository, is designed to facilitate and streamline the +scoping analysis for KBAs identification by leveraging the free and open +access data of GBIF. Before exploring and running the workflow code, I +encourage users to familiarize themselves with the + + Global +Standard for the Identification of Key Biodiversity Areas + + (IUCN, +2016) and the + + Guidelines +for using this standard + + (KBA Standards and Appeals Committee, +2020). +
++ In this workflow, we will focus solely on the following species-based +criteria: +
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- + A1 for globally threatened species + +
- + B for restricted-range species + +
- + D1 for congregations during a particular stage of the life +cycle + +
+ The workflow is divided into + + three main steps + + . +First, the user downloads and cleans GBIF occurrence data. The code then +retrieves information for each species present in the dataset, accessing +the IUCN Red List and Bird Data Zone portals and the tools provided in +the following KBA + + webpage + + . +With this information, the code + + selects the species present in +the study area that have the potential to trigger the aforementioned KBA +criteria. + + The second step is to apply two functions that allow +the user to map the location of the potential trigger species and + + identify the sites where they occur in significant +numbers. + + In the case of bird species, it is also possible to +locate the places where they meet the thresholds of the KBA criteria +considered. The last step of the workflow is to + + obtain the +citations + + of the different sources of information consulted in +the previous steps. +
++ The scheme below represents the overall workflow +
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+ + Fig 1. Scheme representing the main steps of the Locating KBAs workflow +
++ 3. Detailed step-by-step instructions +
++ In the Notebook tab you will find the explanation of all the steps +needed to complete the workflow and their associated code. Additionally, +you will be able to interact with the most important results of the +workflow, such as the table of potential trigger species and the maps of +the sites where they occur in significant numbers. +
++ Example outputs +
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+ The following is a preview of the table to identify potential trigger +species under KBA criteria (globally threatened species, restricted +range species, species that form seasonal congregations). For bird +species, it is also possible to obtain the global number of mature +individuals and calculate the criteria thresholds. +
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+ + The script also contains a function that will help the user +identifying the sites where potential trigger species have large counts +or where there are multiple records of potential trigger species. +
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+ + A second function, available only for bird species, creates an +interactive map showing the location of records that meet the threshold +of individuals for each criterion evaluated. +
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+ + How to run the workflow code in your local computer +
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+ Sign in into your GitHub account + + here + + . +
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+ To copy the repository to your profile, go to the search tab at +the top of the screen and type +[URL Redacted] +
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+ Click on the repository +
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+ Click the fork button in the right corner. +
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+ If you want to open or modify the files and run the code in your +local computer, open the GitHub Desktop software. +
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+ If this is your first time opening the software, it will ask you +to sign into your GitHub account. Once done, select the Locating KBAs +repository and click the clone button at the bottom. +
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- + In the pop-up window, select GitHub.com and set the folder where you +want to save all the files that are in the repository. + +
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+ Select clone +
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+ After GitHub desktop finishes cloning the files to your local +computer, select show in explorer to open the folder where the files +were saved. +
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- + Open the [URL Redacted] RStudio project. In the lower right +corner, select Notebook.Rmd to open the Rmarkdown file. + +
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- + Now you are ready to modify the script and run it! + +
+ Citation +
++
+ Linero D (2021) Locating KBAs: An automated workflow for identifying +potential Key Biodiversity Areas. + + [URL Redacted] + +
++ References +
++
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+ Gueta, T., Carmel, Y (2016). Quantifying the value of user-level +data cleaning for big data: A case study using mammal distribution +models. Ecological Informatics, 34, 139-145. + + https://doi.org/10.1016/j.ecoinf.2016.06.001 + +
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+ IUCN (2016). A Global Standard for the Identification of Key +Biodiversity Areas, Version 1.0. +
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+ KBA Standards and Appeals Committee (2020). Guidelines for using +A Global Standard for the Identification of Key Biodiversity Areas. +
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