Data from: Population genomics, local adaptation, and cryptic speciation in a temperate reef fish, the black surfperch, Embiotoca jacksoni, using genome-wide resequencing
Data files
Sep 29, 2025 version files 2.33 GB
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EJA_GenPop_data.zip
2.33 GB
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EJA_ML_tree.zip
16.05 KB
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README.md
5.81 KB
Abstract
The black surfperch, Embiotoca jacksoni, an apelagic reef fish with limited dispersal due to its lack of a pelagic larval stage, offers a unique model for studying local adaptation and potential cryptic speciation in marine species. This study employs medium-coverage whole genome resequencing to explore population structure, local adaptation, and genetic divergence across a latitudinal gradient from central California to Baja California, Mexico, including offshore islands. We identify strong genetic differentiation between five four distinct groups, a coastal group and four three island groups (Santa Catalina Island, San Clemente Island, and Isla Guadalupe, and Isla San Jerónimo), from Principal Components Analysis (PCA), Fst estimation, and ancestry (sNMF) admixture and phylogenetic analyses. For coastal populations, genetic structure correlates with geographic distance, and isolation-by-distance (IBD) patterns are evident. Further, we identify numerous outlier loci associated with adaptive traits, particularly genes linked to reproductive isolation, such as Spermine oxidase, Izumo sperm-egg fusion protein 1, and SPAG1, which are involved in fertilization success. These loci suggest that reproductive barriers are contributing to divergence among genetic groups. The presence of signatures of both genetic drift and selective pressures, particularly in genes governing fertilization, indicates that these distinct populations may represent incipient or cryptic species. Our findings highlight the role of low dispersal in fostering local adaptation and speciation in marine systems and underscore the potential for rapid evolutionary responses to environmental gradients. This research provides insights into the complex processes of speciation in marine vertebrates, particularly those with limited gene flow, and offers a baseline for monitoring genetic shifts in response to climate change.
Dataset DOI: 10.5061/dryad.nk98sf85h
Description of the data and file structure
The main file of this analysis is EJA_GenPop_data/data/8-Embiotoca_filtered.vcf.gz and all steps leading to these data are in main article's Material&Methods sections Sampling, DNA extraction – Library preparation. and Mapping and Variant calling.
Files and variables
EJA_GenPop_data.zip
EJA_GenPop_data.zip/
└─ EJA_GenPop_data/
├─ BayeScan2.1/ (https://cmpg.unibe.ch/software/BayeScan/). See https://cmpg.unibe.ch/software/BayeScan/files/BayeScan2.0_manual.pdf for more details
│ ├─ EJA_Bayescan.sel ------------------------------------ Text file, table - Output from the MCMC algorithm, each line provides the trace of the parameters estimated by the mode (LogLikelihood and the 15 populations Fst values) for one run.
│ └─ EJA_Bayescan_fst.txt -------------------------------- Text file, table - Results of the BayeScan analysis, for each line representing a loci and will display the folowing information: The index of the locus corresponding to the index in the input file; The posterior probability for the model including selection; The logarithm of Posterior Odds to base 10 for the model including selection. Note that this value is arbitrarily fixed to 1000 when the posterior probability is 1 (should be infinity); The estimated alpha coefficient indicating the strength and direction of selection. A positive value of alpha suggests diversifying selection, whereas negative values suggest balancing or purifying selection; The FST coefficient averaged over populations. In each population FST is calculated as the posterior mean using model averaging.
├─ data/
│ ├─ 8-Embiotoca_filtered.vcf.gz ------------------------- Compressed variant call format - genomic data from 144 *Embiotoca jacksoni* individuals collected along the Californian (US and Baja) coast.
│ ├─ individuals_coordinates.csv ------------------------- Comma-separated values - Metadata for the 144 indivuals collected (sample ID, localisation, longitude, latitude, population)
│ ├─ Individuals_plot_order.txt -------------------------- Text file - Help file for plotting purpose
│ └─ population_map_bsc.txt ------------------------------ Text file - Population map for data conversion from .vcf to .geste (BayeScan), each line matches a sample ID to its population.
└─ output/
├─ admixture/
│ ├─ 8-Embiotoca_filtered_r8.8.Q ---------------------- Text file, table - Lines represent individuals and columns the proportion of ancestry of each inferred ancestral population. 8th run of sNMF using K = 8 parameter (amount of column), all ancestry percentages for the 144 individudals sampled (amount of lines)
│ ├─ 8-Embiotoca_filtered_r16.5.Q --------------------- Text file, table - Lines represent individuals and columns the proportion of ancestry of each inferred ancestral population. 16th run of sNMF using K = 5 parameter (amount of column), all ancestry percentages for the 144 individudals sampled (amount of lines)
│ ├─ 8-Embiotoca_filtered_r16.6.Q --------------------- Text file, table - Lines represent individuals and columns the proportion of ancestry of each inferred ancestral population. 16th run of sNMF using K = 6 parameter (amount of column), all ancestry percentages for the 144 individudals sampled (amount of lines)
│ └─ 8-Embiotoca_filtered_r46.7.Q --------------------- Text file, table - Lines represent individuals and columns the proportion of ancestry of each inferred ancestral population. 46th run of sNMF using K = 7 parameter (amount of column), all ancestry percentages for the 144 individudals sampled (amount of lines)
├─ EJA_allele_frequencies_tb.rds ----------------------- R data file, data frame - Results from running the makefreq() function of the adegenet 2.1.11 R package formated as a data frame. Each pair of line are the two alleles of each SNPs, they are identified by the first 3 columns of the table (CHROM, POs and ALLELE) and the subsequent column represent the allele frequencies in each of the 15 studied populations.
└─ EJA_lcwgs_Edward_distance.rds ----------------------- R data file, dist R object - Raw results from running the dist.genpop() function of the adegenet 2.1.11 R package. Pairwise distance matrix from the 15 populations. Use as.matrix() to see the actual distance matrix (symmetrical with null coefficient on the diagonal).
EJA_ML_tree.zip
EJA_ML_tree.zip/
├─ 8-Embiotoca.pruned.min4.fasta.varsites.phy.contree -------- Text file - Consensus tree with assigned branch supports where branch lengths are optimized on the original alignment from running the [IQ-TREE software version 2](https://github.com/iqtree/iqtree2)
├─ 8-Embiotoca.pruned.min4.fasta.varsites.phy.iqtree --------- Text file - Full result of the run, this is the main report file from running the [IQ-TREE software version 2](https://github.com/iqtree/iqtree2) (All the estimated model parameters for each tree and the tree weights are shown in this file)
└─ Create_ML_tree_with_IQ-TREE_EJA.md ------------------------ Text file - Full code for the phylogenetic analysis, can also be find at [jtoy7/EJA_lcwgs](https://github.com/jtoy7/EJA_lcwgs)
Code/software
All codes and scripts used for the analyses can be found at https://doi.org/10.5281/zenodo.17076840 and https://github.com/jtoy7/EJA_lcwgs.
