DOI: 10.5061/dryad.hqbzkh1v5
title: Bridging the Scotia Arc: Climate-driven shifts in connectivity of the freshwater crustacean <em>Branchinecta gaini</em> in sub-Antarctic and Antarctic ecosystems

description: The datasets presented here correspond to the three methodologies
 combined. First, it corresponds to obtaining mitochondrial DNA sequences
 (cox1 and 16S). Second, the SNPs obtained on the basis of the filtering
 described in the methodology and finally the occurrences used for the
 niche models. All these data correspond to the species Branchinecta gaini,
 an Anostraco throughout its distribution in the southern part of South
 America, South Georgia and maritime Antarctica.
description: Environmental data We extracted 19 bioclimatic
 variables and the bioscd variable from the CHELSA database (www.chelsa-climate.org), with a spatial resolution of approximately 30 arc-seconds (~1 km) (Karger, et al. 2017). These variables were clipped to the study area which, in the case of Antarctica, corresponds to ice-free areas. The current ice-free layer was defined using the rock_outcrop_high_res_polygon (Burton-Johnson, et al. 2016) from the Antarctic Digital Database (ADD version 7; http://www.add.scar.org). For future projections, we used ice-free area data provided by Lee, et al. (2017). To assess multicollinearity among predictor variables, we calculated Pearson correlation coefficients and excluded variables with r-values &gt; 0.75 to ensure the independence of the predictors. The final set of variables included: BIO1 (mean annual air temperature); BIO5 (mean daily maximum air temperature of the warmest month); BIO6 (mean daily minimum air temperature of the coldest month); BIO12 (annual precipitation); BIO15 (precipitation seasonality); and BIO SCD (snow cover days). DNA sequences, SNP calling and filtering To address historical and contemporary connectivity across the distribution of <em>B.gaini</em>, we conducted analyses using two datasets. For the historical perspective, we used partial fragments of two mitochondrial genes (cox1 and 16S rRNA), and for more recent genetic connectivity, we used Reduce Representation Sequencing (RRS) technique to produce Single Nucleotide Polymorphisms (SNP). Mitochondrial genes were amplified using PCR (electronic supplementary material, table S2). Amplicons were purified and sequenced in both directions by Macrogen Inc. (Santiago, Chile). Alignments were obtained using Geneious R10 (https://www.geneious.com). For RSS, samples were sequenced through a genotyping-by-sequencing (GBS) method at the Biotechnology Center in the University of Wisconsin using, after optimization, the PstI/MspI restriction enzymes. After enzyme digestion, each DNA fragment was linked to a barcode adaptor to recognize it <em>in silico</em> and libraries were prepared using a HiSeq2000 (Illumina, USA) platform. Reads were visualized in FastQC 0.10.1 for quality checking. SNP-calling was carried out with the pipeline Universal Network-Enabled Analysis Kit (UNEAK) in Tassel v. 3 (Lu, et al. 2013). We used a minor allele frequency of 0.05, a minimum proportion of sites present of 0.7, and a site minimum call rate of 0.75, to ensure that at least 75% of the individuals in each SNP were covered for at least one tag. After filtering, we estimated Hardy–Weinberg equilibrium (HWE) deviations per locus and per population with Arlequin 3.5.2.2 (Excoffier and Lischer 2010) using 10 000 permutations. <em>p</em>-values were corrected with a false discovery rate (FDR) correction (q-value = 0.05), and SNPs that appeared in HW disequilibrium in at least 60% of the populations were removed from the dataset. This approach ensures the reliability of our inferences regarding population structure, avoiding sequencing artefacts, in alignment with recommended practices for population genetics in non-model organisms (Pearman, et al. 2022). To retain only neutral markers, we used two population differentiation analyses to identify SNPs potentially under diversifying selection. These SNPs were eliminated from the final dataset. The first analysis was performed using the pcadapt v.4.3.3 (Luu, et al. 2017) R package. This approach uses a Principal Component Analysis (PCA) to detect population structure. Then, each SNP is regressed at the principal components (PCs) retained. Here, 10 PCs were retained based on their eigenvalues following (Cattell 1966). We applied a statistical test to the PCA when regressing SNPs with the PCs and a cut-off of q-value = 0.05 was selected to assign the outliers. This analysis is not impacted by admixed individuals, because pcadapt does not require grouping individuals into populations (Luu, et al. 2017). Second, we used an FST outlier approach implemented in Bayescan 2.1 (Foll and Gaggiotti 2008), which uses a Bayesian method to estimate the probability of each locus being under the influence of selection. Considering that such loci tend to be highly differentiated and exacerbate the genetic structure, those identified were not considered for analyses. A total of five separate runs were performed with 500,000 iterations, 10% burn-in period and a prior odds of 1000 were used. To avoid the occurrence of false positives using both approaches, a FDR correction of q-value = 0.05 was applied. Outliers detected by either or both approaches were eliminated (filtered) from the final dataset, with the aim of highlighting demographic isolation and assessing connectivity patterns. After filtering, a final dataset of 7446 non-outlier SNPs genotyped from 186 individuals was obtained.
description: # Bridging the Scotia Arc: Climate-driven shifts in connectivity of the
 freshwater crustacean *Branchinecta gaini* in sub-Antarctic and Antarctic
 ecosystems ## Dataset Overview This dataset contains the data and code
 required to replicate analyses in Branchinecta gaini (in review), testing
 the hypothesis of range shift due to climate change using historical and
 contemporary genetic structure and niche modeling. Data cover 20 sampling
 lakes and freshwater ponds within Southern South America, the subantarctic
 island of South Georgia, and maritime Antarctic islands and archipelagos.
 Genetic data include a dataset of single-nucleotide polymorphisms (SNPs)
 and two loci of mitochondrial DNA. ### Dates of Data Collection A.
 Branchinecta sampling: 2017 - 2022 B. Branchinecta DNA extraction:
 2021-2023 C. Genetic sequencing data: 2021-2024 ### Ethics Approval
 Sampling protocol and permission of collected material in Antarctic
 regions were conducted under INACH environmental regulations. Collections
 activities outside Antarctica territory were performed following the
 Chilean Ministerio de Economía, Fomento y Turimo regulations under the
 permit numbers Nº E-2021-261, granted by the Subsecretary of Fishery and
 Aquaculture. ### Files and Folders **Bgaini_16S_101Seq.nex** This file
 contains 101 sequences of 16S rRNA in nex format to conduct different
 genetic diversity indices. **Bgaini_cox1_294seq.nex** This file contains
 294 sequences of cox1 of Branchinecta gaini from South America,
 Subantarctic islands, and Maritime Antarctica. The file is in the nex
 format to conduct different genetic diversity indices, DiYABC analyses,
 demographic inferences, and phylogenetic reconstructions.
 **Bran_186_8912_2plates_mC75_mAF001.vcf** This Variant Call Format (VCF)
 file contains Single Nucleotide Polymorphism (SNP) data from 186
 individuals across subantarctic and Antarctic sampling locations. It
 comprises 8912 SNPs obtained from SNP calling in Tassel.
 **Supplementary_material_TableS1.xlsx** List of *Branchinecta gaini*
 records detailing the type of analysis for which it was used, following
 FAIR criteria. Population identifier (IDpop): Six biogeographic regions
 are presented (1) SSA i) ARG: Argentinean Patagonia, ii) MSE: Magellanic
 Subantarctic Ecoregion, (2) SG: South Georgia, (3) SOI: South Orkney
 Islands, (4) SSI: South Shetland Islands, (5) EAP: East Antarctic
 Peninsula, (6) WAP: West Antarctic Peninsula. ENM: Ecological Niche
 Modelling. Three biogeographic provinces are presented: (1) South America,
 (2) Subantarctic island, (3) maritime Antarctica.
 **OccurrenceBgaini_ENM.xlsx** List of Branchinecta gaini records used in
 the ENM analysis containing ID, decimalLatitude, and decimalLongitude data
 only. **Supplementary_material_S3.docx** Molecular protocol for cox1 and
 16S PCR. Details of the mix preparation, thermal cycling parameters, and
 primer sequences. **Supplementary_material_S5.reftableHeader** This file
 contains the values of the different scenarios and prior distribution
 parameters employed in DIYABC v2.1.  **SupplementaryMaterialBgaini.docx**
 Supplementary tables and figures are listed throughout the paper (S2, S4,
 S6, S7, S8, S9, S10 & S11).