DOI: 10.5061/dryad.bk3j9kdkm
title: Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework

description: Under the recently adopted Kunming-Montreal Global Biodiversity Framework,
 196 Parties committed to report the status of genetic diversity for all
 species. To facilitate reporting, three genetic diversity indicators were
 developed, two of which focus on processes contributing to genetic
 diversity conservation: maintaining genetically distinct populations and
 ensuring populations are large enough to maintain genetic diversity. The
 major advantage of these indicators is that they can be estimated with or
 without DNA-based data. However, demonstrating their feasibility requires
 addressing the methodological challenges of using data gathered from
 diverse sources, across diverse taxonomic groups, and for countries of
 varying socioeconomic status and biodiversity levels. Here, we assess the
 genetic indicators for 919 taxa, representing 5,271 populations across
 nine countries, including megadiverse countries and developing economies.
 Eighty-three percent of taxa assessed had data available to calculate at
 least one indicator. Our results show that although the majority of
 species maintain most populations, 58% of species have populations too
 small to maintain genetic diversity. Moreover, genetic indicator values
 suggest that IUCN Red List status and other initiatives fail to assess
 genetic status, highlighting the critical importance of genetic
 indicators.
description: Data comes from the first multi-country assessment of genetic
 diversity status, with emphasis on the PM and Ne 500 indicators, including
 nine countries: Australia, Belgium, Colombia, France, Japan, Mexico, South
 Africa, Sweden, and the United States of America. Within each country,
 teams of researchers and conservation practitioners from academia,
 government institutions, and non-governmental organizations aimed to asses
 of 50-100 species per country. In total 919 taxa, representing 5,271
 populations were assessed. Data comes from different sources depending on
 the country and the species. Data was collected using a
 KoboToolBox (https://www.kobotoolbox.org/) form specifically designed for
 this project. The resulting dataset was downloaded as a .csv file and
 processed in R version 4.2.1 using custom functions and a processing
 pipeline specifically developed for this study for quality checking,
 indicator calculation, and subsequent analyses. The R code is available
 from  https://github.com/AliciaMstt/GeneticIndicators or a static version
 at Zenodo: https://zenodo.org/records/10620307
 Japan's raw data comes from the ancillary data for national
 red-list evaluation basically based on a field survey provided by the
 Japanese Society for Plant Systematics. Raw data was provided on the
 condition that no information that could lead to the species'
 location in the field would be published, as a way to protect the
 endangered species. Therefore, for this repository part of the Japan data
 was obscured or removed, as explained for each variable in the data
 dictionary.
description: # Multinational evaluation of genetic diversity indicators for the
 Kunming-Montreal Global Biodiversity Monitoring Framework
 [https://doi.org/10.5061/dryad.bk3j9kdkm](https://doi.org/10.5061/dryad.bk3j9kdkm) Data comes from the first multi-country assessment of genetic diversity status, with emphasis on the PM and Ne 500 indicators, including nine countries: Australia, Belgium, Colombia, France, Japan, Mexico, South Africa, Sweden, and the United States of America. For all countries, some of the data collected was not cleaned or analysed in the associated publication, thus the variables are shared so that the processing scripts can run, but the values were removed. Data for these variables would be published as part of a follow-up paper. See the data dictionary for details. ## Code/Software Data was collected using a KoboToolBox (https://www.kobotoolbox.org/ form specifically designed for this project. The resulting dataset was downloaded as a .csv file and processed in R version 4.2.1 using custom functions and a processing pipeline specifically developed for this study for quality checking, indicator calculation, and subsequent analyses. The R code is available from  https://github.com/AliciaMstt/GeneticIndicators or a static version at Zenodo: https://zenodo.org/records/10620307 ## Description of the data and file structure Note on *.csv files: Open them with "UTF-8" encoding to properly see special characters of non-English languages present in the text. * **kobo_form.xlsx**: xlsx version of the Kobo form (set of questions that participants answered for each species, just the questions). This form can be imported into KoboToolBox to start a new blank project. * **genetic_diversity_indicators_dictionary_dryad.xlsx**: data dictionary explaining the meaning of each variable in the Kobo form and data files. The tab **"kobo_form_variables"** outlines variables in the kobo form and kobo outputs, including output files (kobo_output_clean.csv, ind1_data.csv, ind2_data.csv, ind3_data.csv, indicators_full.csv, metadata.csv), including the variable name as recorded in the output data, a variable description of each variable, and an example of real output values. The column comments contain comments on how the data was used in the associated paper of this repository, or reasons why the data could not be shared (e.g. because it contains sensitive information of endangered species). The tab **"values_for_categ_variables"** contains all the options available to the user for Kobo questions where the question type was "select_one" (user can select only one provided value option) or "select_multiple" (user can select one or more provided value options). The question_id is the unique identifier linked to a kobo question (see kobo_form_variables tab for information). The tab **"processing_scripts_variables"** describes variables created by the R functions or processing scripts provided in the GitHub repository https://github.com/AliciaMstt/GeneticIndicators as part of processing the kobo_output_clean.csv data. They are present in the output files ind1_data.csv, ind2_data.csv, ind3_data.csv, indicators_full.csv OR metadata.csv * **International_Genetic_Indicator_testing_V_4.0_-latest_version-False-_2023-11-02-08-23-26.csv**: raw output of the Kobo form (answers to the form), as downloaded from the KoboToolBox platform. Notice that the original separator was ";", but in this file, the separator was converted to "," after obscuring Japan's data. * **kobo_output_clean.csv**: "clean" kobo output data after processing the raw Kobo output with the script 2_cleaning.Rmd (see associated Github repository) to correct errors detected by `1_quality_check.Rmd, based on the feedback from the people who collected the data. The output kobo_output_clean.csv has the clean data that was used for analyses. * **processed_files.zip**: zip file containing the population data for the species with &gt;25 populations, which was submitted using a text template instead of the Kobo form. The files in "processed_files/original_files/* " are the files as they were submitted, and the files in "processed_files/processed_files/* " are the corrected version using the process_attached_files() function (see associated Github repository) to make them compatible with how population data as expected to estimate the Ne &gt;500 indicator. * **metadata.csv**: a subset of variables from kobo_output_clean.csv after running `get_metadata()` (see associated Github repository) to extract the metadata for taxa and indicators, in some cases creating new useful variables, like taxon name (joining Genus, species, etc) and if the taxon was assessed only a single time or multiple times. Each row is a taxon. * **ind1_data.csv**: data needed to estimate the Ne 500 indicator (the proportion of populations within species with an effective population size Ne greater than 500) in "long" format because, in the Kobo output, population data is in different columns. Data in this file comes from re-formatting the kobo_output_clean.csv data using the `get_indicator1_data()` function (see associated Github repository) so that population data is in rows and after running `transform_to_Ne()` to get the Nc data from point or range estimates and transforms it to Ne multiplying for a ratio Ne:Nc. This is needed for downstream analyses. In the **ind1_data.csv** file, each population is a row, and there are as many rows per taxon as there are populations within it. * **ind1_data_from_templates.csv**: similar to ind1_data.csv, but for the species with &gt;25 populations (see processed_files.zip). * **ind3_data.csv**: subset of variables from kobo_output_clean.csv after running `get_indicator3_data()` (see associated Github repository) to extract the data needed to estimate DNA-based genetic monitoring indicator (number of species in which genetic diversity has been or is being monitored using DNA-based methods). Each row is a taxon. * **indicators_full.csv**: output of the script 3_manuscript_figures_analyses (see associated Github repository) stating the indicator value for indicator1 (Ne 500 indicator) and indicator 2 (proportion of maintained populations or PM indicator) and some metadata for each species assessment. Each row is an assessment of a species.