DOI: 10.5061/dryad.s4mw6m9d8
title: Dataset and R code: Genetic diversity of lion populations in Kenya: evaluating past management practices and recommendations for future conservation actions by Chege M et.al

description: The decline of lions (Panthera leo) in Kenya has raised conservation
 concerns on their overall population health and long-term survival. This
 study aimed to assess the genetic structure, differentiation, and
 diversity of lion populations in the country, while considering the
 influence of past management practices. Using a lion-specific Single
 Nucleotide Polymorphism (SNP) panel, we genotyped 171 individuals from 12
 populations representative of areas with permanent lion presence. Our
 results revealed a distinct genetic pattern with pronounced population
 structure, confirmed a north-south split, and found no indication of
 inbreeding in any of the tested populations. Differentiation seems to be
 primarily driven by geographical barriers, human presence, and climatic
 factors, but management practices may have also affected the observed
 patterns. Notably, the Tsavo population displayed evidence of admixture,
 perhaps attributable to its geographic location as a suture zone, vast
 size, or to past translocations, while the fenced populations of Lake
 Nakuru National Park and Solio Ranch exhibited reduced genetic diversity
 due to restricted natural dispersal. The Amboseli population had a high
 number of monomorphic loci likely reflecting a historical population
 decline. This illustrates that patterns of genetic diversity should be
 seen in the context of population histories, and that future management
 decisions should take these insights into account. To address the
 conservation implications of our findings, we recommend prioritizing the
 maintenance of suitable habitats to facilitate population connectivity.
 Initiation of genetic restoration efforts and separately managing
 populations with unique evolutionary histories is crucial for preserving
 genetic diversity and promoting long-term population viability.
description: This dataset was obtained from 12 kenyan lion populations. After
 DNA extraction, SNP genotyping was performed using an allele-specific KASP
 technique. The attached datasets includes the .txt  and
 .str versions of the autosomal SNPs to aid in reproducing the
 results.  
description: # dataset and r code associated with the publication entitled
 "Genetic diversity of lion populations in Kenya: evaluating past
 management practices and recommendations for future conservation
 actions" by Chege M et.al.
 [https://doi.org/10.5061/dryad.s4mw6m9d8](https://doi.org/10.5061/dryad.s4mw6m9d8)    We provide the following description of the dataset and scripts for analysis carried out in R: We have split the data and scripts for ease of reference i.e.,  1.) Script 1: titled ‘***Calc_He_Ho_Ar_Fis’***. For calculating the genetic diversity indices i.e. allelic richness (AR), Private alleles (AP), Inbreeding coefficients (FIS), expected (HE) and observed heterozygosity (HO). This script uses: * **“data_HoHeAr.txt”** dataset. This dataset has information on individual samples, including their geographical area (population) of origin and the corresponding 335 autosomal single nucleotide polymorphism (SNP) reads. * ‘***shompole2.txt’***  this bears the dataset from the Shompole lion population that was separated from “data_HoHeAr.txt” file. SNP 81 and 82 were removed from this file as they were causing an error in the calculation of allelic richness. * ‘***diversityKenya_lions.csv’*** data file contains the genetic diversity scores for each of the 12 populations for carrying out the two sample t-test to test for significant genetic differences between closed and open lion populations. 2.) Script 2:titled ‘***Calc_PCA_DAPC_Kenya’***. For calculating Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC). This script uses: *   “***data_DAPC_PCA.txt***” dataset. This dataset also has information on the individual samples, including their geographical area (population) of origin and the corresponding 335 autosomal single nucleotide polymorphism (SNP) reads. In this dataset we removed sample Ke02 as it was an outlier.                 3.) Script 3: titled ‘***supplementary_snmf’***. The code detailed in this script was used to estimate individual ancestry coefficients using Sparse Nonnegative Matrix Factorization algorithms (sNMF). To run this analysis GPS coordinates of the corresponding samples are required, we however cannot provide these due to the threatened status of lions in Kenya. We have made the script quite self explanatory, it follows similar methodology as that is detailed in François 2016 i.e. [http://membres-timc.imag.fr/Olivier.Francois/tutoRstructure.pdf.](http://membres-timc.imag.fr/Olivier.Francois/tutoRstructure.pdf.)