Data from: Identifying Key Biodiversity Areas based on distinct genetic diversity

Description

Notes

Methods

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⁺, D_est , lambda corrected for sample size , and N_e. 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 N_e could be calculated to allow a proper comparison of all methods.

To explore similarities between the different approaches, correlations between the results of all six methods, allelic overlap, AMOVA, D⁺, D_est, N_e, and lambda_cor, were calculated in R. To increase the understanding between diversity measuring methods, 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, D⁺, D_est, N_e, and l_cor, are based on different sample sizes, since N_e could not be calculated for some areas.

We applied the A1b (> 1 % of the global distinct genetic diversity occurs at this site) and B1 (> 10 % of the global distinct genetic diversity occurs at this site) threshold to our studied methods. 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 from the sum of the distinct genetic diversity of all locations. Areas lacking N_e were allocated the median of remaining areas.

Structure analyses were conducted in addition to the calculation of the six metrics for two case studies: the Chinook salmon (Oncorhynchus tshawytscha; Gomez-Uchida et al. 2019) and the Tenerife Short-winged Bush-cricket (Ariagona margaritae). The latter data set has not yet been published for another project. The site selection was based upon KBA criterion B1. The results were processed in R. Afterwards maps were created.

To create the Tenerife Short-winged Bush-cricket (Ariagona margaritae Kraus, 1892) 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 N_e, the smallest possible natural number was added to transform all N_e into positive numbers. Apart from that, this dataset was analyzed in the same way as the previously used datasets.