Identifying Key Biodiversity Areas based on distinct genetic diversity

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, average taxonomic distinctness (AvTD, Δ+), and effective population size (Ne), calculated with the two different programs SPEED-NE and NEESTIMATOR, 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 as it encompasses both genetic distinctiveness and diversity, is based on simple allele abundances, and can ..., 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 , Delta+, Dest , lambda corrected for sample size , and Ne. 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 > 20% missing data were removed from the dataset. Missing allele abundances were replaced with the means of remaining alleles. Datasets were only analyzed if Ne could be calculated to allow a proper com..., , **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 introdu...,