DOI: 10.5061/dryad.95x69p8tt
title: Widespread admixture blurs population structure and confounds Lake Trout (Salvelinus namaycush) conservation even in the genomic era

description: Intraspecific variation is important for species’ long-term persistence,
 and a main conservation target. Units below the species level are often
 identified based on evidence for adaptive divergence and reproductive
 isolation. For complexchallenging taxa, population genomics has the
 potential to improve management strategies by facilitating the
 identification of genetic boundaries and adaptive variation between
 discrete units. This paper examines intraspecific divergence of Lake Trout
 (Salvelinus namaycush) in Great Slave Lake (GSL), Canada, using
 low-coverage whole-genome sequencing data. Specifically, we evaluate
 genetic differentiation and assess the relationship with morphological,
 mitochondrial, and putatively adaptive divergence. We show that at least
 three genetically distinct Lake Trout populations co-occur in GSL and
 exhibit differences in spatial distribution and body size, with signatures
 of selection. However, admixture was widespread (60% of the fish). These
 findings highlight that, even in the era of whole genome sequencing,
 identifying discrete population units can remain challenging in systems
 where gene flow among genetically distinct populations is ubiquitous. To
 give more recognition to this complexity, shifting the focus of management
 efforts from discrete populations (i.e., intraspecific units) to the area
 where evolutionary acts are at play could be beneficial to protect
 species’ resilience and adaptive potential in some natural systems.
description: # Widespread admixture blurs population structure and confounds Lake Trout
 (Salvelinus namaycush) conservation even in the genomic era
 [https://doi.org/10.5061/dryad.95x69p8tt](https://doi.org/10.5061/dryad.95x69p8tt) ## Description of the data and file structure **README** for "Widespread admixture blurs population structure and confounds Lake Trout (*Salvelinus namaycush*) conservation even in the genomic era". The following files are included: * Sample metadata (Individuallevel_metadata\_ GSL_LakeTrout_V2.csv) * NGSadmix Q values (Individuallevel_NGSadmix\_ GSL_LakeTrout_V2.csv) * Mitochondrial DNA sequences (mitochondrialDNA_sequences_GSL_LakeTrout.txt) * Nucleotide diversity and Tajima’s D (Populationlevel_Diversity_Estimates_GSL_LakeTrout_V2.csv) ### Files and variables #### File: Individuallevel\_metadata\_\_GSL\_LakeTrout\_V2.csv **Description:** Metadata ##### Variables * sample: Sample ID * area: Management area (IW, IE, II, III, IV, V, VI) * sampled_day: Sampling date: Day * sampled_month: Sampling date: Month * sampled_year: Sampling date: Year * sample_origin: Sample source (who collected the sample) * water_depth: Depth of capture in meters (m) * length: Fork length in millimetres (mm) * weight: Weight in grams (g) * sex: Sex (Male or Female assessed on the field) #### File: Individuallevel\_NGSadmix\_\_GSL\_LakeTrout\_V2.csv **Description:** NGSAdmix output ##### Variables * sample: Sample ID * K2_Q90: Genetic cluster with 2 population assumed (K2) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K3_Q90: Genetic cluster with 3 population assumed (K3) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K4_Q90: Genetic cluster with 4 population assumed (K4) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K5_Q90: Genetic cluster with 5 population assumed (K5) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K6_Q90: Genetic cluster with 6 population assumed (K6) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K7_Q90: Genetic cluster with 7 population assumed (K7) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K8_Q90: Genetic cluster with 8 population assumed (K8) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K9_Q90: Genetic cluster with 9 population assumed (K9) - "GCx" for fish with Q&gt;=0.90 with x being the cluster number and "admix" for Q&lt;0.90 * K2_cluster: Genetic cluster with 2 population assumed (K2) - Maximum Q value determine cluster assignment * K3_cluster: Genetic cluster with 3 population assumed (K3) - Maximum Q value determine cluster assignment * K4_cluster: Genetic cluster with 4 population assumed (K4) - Maximum Q value determine cluster assignment * K5_cluster: Genetic cluster with 5 population assumed (K5) - Maximum Q value determine cluster assignment * K6_cluster: Genetic cluster with 6 population assumed (K6) - Maximum Q value determine cluster assignment * K7_cluster: Genetic cluster with 7 population assumed (K7) - Maximum Q value determine cluster assignment * K8_cluster: Genetic cluster with 8 population assumed (K8) - Maximum Q value determine cluster assignment * K9_cluster: Genetic cluster with 9 population assumed (K9) - Maximum Q value determine cluster assignment * K2_1: Q values at K2 (cluster #1) * K2_2: Q values at K2 (cluster #2) * K3_2: Q values at K3 (cluster #2) * K3_1: Q values at K3 (cluster #1) * K3_3: Q values at K3 (cluster #3) * K4_3: Q values at K4 (cluster #3) * K4_1: Q values at K4 (cluster #1) * K4_4: Q values at K4 (cluster #4) * K4_2: Q values at K4 (cluster #2) * K5_2: Q values at K5 (cluster #2) * K5_1: Q values at K5 (cluster #1) * K5_4: Q values at K5 (cluster #4) * K5_5: Q values at K5 (cluster #5) * K5_3: Q values at K5 (cluster #3) * K6_3: Q values at K6 (cluster #3) * K6_4: Q values at K6 (cluster #4) * K6_5: Q values at K6 (cluster #5) * K6_2: Q values at K6 (cluster #2) * K6_1: Q values at K6 (cluster #1) * K6_6: Q values at K6 (cluster #6) * K7_5: Q values at K7 (cluster #5) * K7_1: Q values at K7 (cluster #1) * K7_2: Q values at K7 (cluster #2) * K7_6: Q values at K7 (cluster #6) * K7_4: Q values at K7 (cluster #4) * K7_3: Q values at K7 (cluster #3) * K7_7: Q values at K7 (cluster #7) * K8_2: Q values at K8 (cluster #2) * K8_1: Q values at K8 (cluster #1) * K8_6: Q values at K8 (cluster #6) * K8_8: Q values at K8 (cluster #8) * K8_5: Q values at K8 (cluster #5) * K8_7: Q values at K8 (cluster #7) * K8_3: Q values at K8 (cluster #3) * K8_4: Q values at K8 (cluster #4) * K9_1: Q values at K9 (cluster #1) * K9_9: Q values at K9 (cluster #9) * K9_4: Q values at K9 (cluster #4) * K9_5: Q values at K9 (cluster #5) * K9_8: Q values at K9 (cluster #8) * K9_2: Q values at K9 (cluster #2) * K9_3: Q values at K9 (cluster #3) * K9_6: Q values at K9 (cluster #6) * K9_7: Q values at K9 (cluster #7) #### File: Populationlevel\_Diversity\_Estimates\_GSL\_LakeTrout\_V2.csv **Description:** ANGSD output ##### Variables * pos: (indexStart,indexStop)(firstPos_withData,lastPos_withData)(WinStart,WinStop) * Chr: Chromosome number * WinCenter: Central position of the window-based analysis * tW: Thetas, estimator: Watterson * tP: Thetas, estimator: pairwise * tF: Thetas, estimator: Fuli * Tajima: Tajima's D * nSites: Number of sites * GC: Genetic group at K3 (individuals with Q values&gt;=0.90) * tW_nsites: Watterson's thetas corrected for the number of sites (tW/nSites) * tP_nsites: Pairwise's thetas corrected for the number of sites (tP/nSites) #### File: mitochondrialDNA\_sequences\_GSL\_LakeTrout.txt **Description:** Mitochondrial DNA sequences ##### Variables * Sequences for each individual (n=190) ## Access information Other publicly accessible locations of the data: * Raw genomic data available on NCBI (**PRJNA1188166**)