DOI: 10.5061/dryad.573n5tbhk
title: Data from: Identifying Key Biodiversity Areas based on distinct genetic diversity

description: Key Biodiversity Areas (KBAs) are sites that contribute significantly to
 the global persistence of biodiversity. Distinct genetic diversity has
 been introduced as one of the metrics to estimate whether a site holds a
 threshold proportion of a species’ global genetic diversity during the KBA
 identification process. However, genetic data has so far not been used due
 to the lack of thoroughly tested methods and guidance. We tested the
 applicability of Analyses of Molecular Variance (AMOVA), allelic overlap,
 the diversity index Simpson's λ, Δ+, Dest, and effective population
 size (Ne) for identification of KBAs. We conclude that Δ+, a measure that
 has originally been developed to measure taxonomic distinctness of biotic
 communities, performs best in the context of KBA identification reflects
 the unique nature of a species’ genetic diversity, is based on simple
 allele frequencies, and can be easily applied and calculated. AMOVA, Ne,
 allelic overlap, and our modified version of λ, were difficult to apply,
 interpret, or both. Dest is easily applied for measuring genetic
 distinctiveness but not genetic diversity. For this reason, it may not be
 suitable for prioritizing areas for the long-term protection of the
 species. Here, we deposited additional information on which methods, and
 how they were calculated on which data sets, including references. We
 included additional information on how these methods performed. Moreover
 we included raw reads, an already processed .str file and additional
 information of additional data set we created to add a case study to our
 publication. The code we used for our analyzes can be found on GitHub and
 Zenodo. For more information about the code and the detailed procedure,
 view the associated publication, the readme on GitHub, and the
 Supplemental_Information.pdf file we publish here.
description: As both SNP and microsatellite datasets are commonly used to
 analyze intraspecific genetic variance, we tested the performance of our
 chosen analytical approaches on 30 published diploid datasets, of which 15
 used SNPs (with an average of 184 SNP loci) and 15 microsatellite datasets
 (with an average of 31 microsatellite loci). Each dataset was analyzed
 with six methods: AMOVA, allelic overlap , Δ<sup>+</sup>,
 <em>D</em><sub>est</sub> , λ corrected for sample
 size , and <em>N<sub>e</sub>.</em> To apply all
 six methods, an R project was created that makes use of many packages that
 facilitate displaying results and working with genetic data and
 tables. For better comparability between the six
 methods, all datasets were prepared in the same way. Sites with fewer than
 30 individuals were removed from the analysis. Individuals with &gt;
 20% missing data were removed from the dataset. For loci with missing
 genotypes, the missing allele counts were replaced with the mean of the
 observed alleles at that locus across all individuals in the
 dataset. To explore similarities between the different
 approaches, correlations between the results of all six methods, allelic
 overlap, AMOVA, Δ<sup>+</sup>,
 <em>D<sub>est</sub></em>,
 <em>N<sub>e</sub></em>, and
 λ<sub>cor</sub>, were calculated in R. Correlations with
 allelic richness were additionally calculated. Two outliers were removed
 from AMOVA results. A Kendall correlation was chosen. Correlations between
 allelic overlap, allelic richness, AMOVA, Δ<sup>+</sup>,
 <em>D<sub>est</sub></em>,
 <em>N<sub>e</sub></em>, and
 λ<sub>cor</sub>, are based on different sample sizes, since
 <em>N<sub>e</sub></em> could not be calculated for
 some areas. We tested all results against two KBA
 criteria, A1b (&gt; 1 % of the global distinct genetic diversity
 occurs at this site) and B1 (&gt; 10 % of the global distinct genetic
 diversity occurs at this site). For each of the six methods, the
 proportion of distinct genetic diversity was calculated as the simple
 proportion of distinct genetic diversity at each location of the sum of
 all locations, as proposed by the KBA standard for AMOVA results. Areas
 lacking <em>N<sub>e</sub></em> were allocated the
 median of remaining areas to enable the application of KBA criteria
 without inflating the proportion of the remaining sites.
 To illustrate the results and coverage of different genetic
 clusters, Structure analyses were conducted in addition to the calculation
 of the five metrics for two case studies: the Chinook salmon
 (<em>Oncorhynchus tshawytscha;</em> Gomez-Uchida et al. 2019)
 as well as a hitherto unpublished dataset of the Tenerife Short-winged
 Bush-cricket (<em>Ariagona margaritae</em> Kraus, 1892). The
 site selection was based upon KBA criterion B1. The results were processed
 in R using several packages. The maps were created using ArcGIS
 Pro. To create the Tenerife Short-winged Bush-cricket
 dataset specimens were collected 2010–2023 on Tenerife and El Hierro. DNA
 was extracted using the Qiagen DNeasy® Blood & Tissue kit.
 ddRADseq libraries were prepared for paired-end sequencing on a
 High-Output Flow cell of an Illumina NextSeq platform (2 x 75bp). Stacks
 2.6.6 was used to demultiplex, filter, and trim raw reads to 65bp, create
 an assembly and a catalogue of loci to finally identify SNPs (n= 64, -p
 150, -r 1). Default settings were maintained. Individuals containing more
 than 20% of missing data were removed from the analysis. The resulting
 dataset comprised 108 individuals and 5198 loci. No area was excluded from
 the analysis as each had ≥20 sequenced individuals. The allelic overlap
 method was omitted for this dataset due to extensive calculation times.
 For <em>N<sub>e</sub>,</em> the smallest possible
 natural number was added to transform all
 <em>N<sub>e</sub></em> into positive numbers.
 Apart from that, this dataset was analyzed in the same way as the
 previously used datasets.
description: **Identifying KBAs based on distinct genetic diversity** Our article
 explains which method is best to identify KBAs based on the distinct
 genetic diversity metric in the KBA standard. To archieve we used 30
 different datasets to calculated six different methods: allelic overlap,
 AMOVA, Delta+, Dest, lambda, and Ne. We also included 2 case studies. One
 of the case studies was done on one of the 30 data sets and the other case
 study was performed on our own data set. Our own data set is from the
 species Ariagona magaritae. We included collection information, and
 barcodes for our won dataset. Code was deposited on github
 ([https://github.com/TheC0der856/genetic_distinct_diversity](https://github.com/TheC0der856/genetic_distinct_diversity)) and zenodo. **Description of the data and file structure** Supplemental_Information.pdf contains all relevant information: all SuppTabs and SuppFigs including descriptions of the data und it also places them in the context of our paper and is introducing an order to our supplementary files. We recommend to open Supplemental_Information.pdf in the beginning. **Files and variables** Descriptions of tables and figures, additional information supporting the article (Zenodo): Supplemental_Information.pdf All figures included in the Supplemental_Information.pdf were added as pdf and jpg format (Zenodo): * SuppFig1.pdf * SuppFig2.pdf * SuppFig3.pdf All tables included in the Supplemental_Information.pdf were added in txt, csv, or ods format (Zenodo): * SuppTab1.txt * SuppTab3.csv * SuppTab2.ods * SuppTab4.csv These are the raw reads of the Ariagona margaritae dataset (Dryad): * 231201_NB501850_A_L1_4_AZJH_1_R1.fastq * 231201_NB501850_A_L1_4_AZJH_1_R2.fastq * 231201_NB501850_A_L1_4_AZJH_2_R1.fastq * 231201_NB501850_A_L1_4_AZJH_2_R2.fastq processed Ariagona margaritae dataset: Supp_Ariagona_dataset.str (Dryad) **Code/software** Tables can be opened in Excel or LibreOffice Calc. csv files can be opened additonaly in Wordpad, text editor, and Notepad++. pdf files can be opened in Adobe Acrobat, LibreOffice Draw, or in your browser. A str file can be opended with Notepad++, but it can be also read into R with the adegenet package. The fastq files are huge and we only recommend to work with them on a cluster with programms such as Stacks. We conducted our analyses with Stacks using bash commands. Apart from this all our code was written in R. **Access information** The data are openly available in Dryad under the Creative Commons Zero (CC0) public domain dedication. No access restrictions apply. Our code is available in github under the MIT Licence.