DOI: 10.5061/dryad.2ngf1vhsw
title: Biogeographic inferences across spatial and evolutionary scales

description: The field of biogeography unites landscape genetics and phylogeography
 under a common conceptual framework. Landscape genetics traditionally
 focuses on recent-time, population-based, small geographic scale, spatial
 genetics processes, while phylogeography typically investigates deep past,
 lineage- and species-based processes at large geographic scales. Here, we
 evaluate the link between landscape genetics and phylogeographic methods
 using the Western Fence lizard (Sceloporus occidentalis) as a model
 species. First, we conducted replicated landscape genetics studies across
 several geographic scales to investigate how population genetics
 inferences change depending on the spatial extent of the study area. Then,
 we carried out a phylogeographic study of population structure at two
 evolutionary scales informed by inferences derived from landscape genetics
 results to identify concordance and conflict between these sets of
 methods. We found significant concordance in landscape genetics processes
 at all but the largest geographic scale. Phylogeographic results indicate
 major clades are restricted to distinct river drainages or distinct
 hydrologic regions. At a more recent timescale, we find minor clades are
 restricted to single river canyons in the majority of cases, while the
 remainder of river canyons include samples from at most two clades.
 Overall, the broad scale pattern implicating stream and river valleys as
 key features linking populations in the landscape genetics results, and
 high degree of clade specificity within major topographic subdivisions in
 the phylogeographic results, is consistent. As landscape genetics and
 phylogeography share many of the same objectives, synthesizing theory,
 models, and methods between these fields will help bring about a better
 understanding of ecological and evolutionary processes structuring genetic
 variation across space and time.
description: ddRAD LIBRARY PREPARATION AND GENOMIC SEQUENCING
 We followed Peterson et al.'s (2012) method for genomic
 library preparation, with some modifications. For each individual, we
 extracted high-molecular-weight genomic DNA using a standard
 phenol-chloroform extraction protocol (Tsai et al. 2019). We measured DNA
 concentrations using a Qubit fluorometer, and for each sample, we digested
 0.5 μg of DNA for 3 hours with <em>SbfI</em> and
 <em>NIaIII</em> restriction enzymes. We then purified these
 fragments with Agencourt AMPure beads before ligation of barcoded Illumina
 adaptors onto the fragment ends. All barcodes differed by at least two
 base pairs to reduce duplexing error rates. We then pooled equimolar
 amounts of each sample before conducting size selection using a Pippin
 Prep to select fragments between 400 and 550 bp in length. We used
 proofreading Taq and Illumina’s index primers for final library
 amplification for 8-10 cycles to reduce PCR bias. We quantified the final
 library concentration using a Qubit fluorometer at high sensitivity.
 Samples were packed on dry ice and sent to the Vincent J. Coates Genomics
 Sequencing Lab at UC Berkeley for sequencing. Quantitative PCR was used to
 determine concentration of adapter-associated fragments, and a BioAnalyzer
 run confirmed fragment sizes as a quality control measure prior to
 sequencing. Final libraries were sequenced (100-bp or 150-bp, single-end
 runs) on Illumina HiSeq 4000 and NovaSeq SP lanes in a total of four
 sequencing lanes. BIOINFORMATICS PROTOCOLS FOR SNP
 DATA We inspected raw Illumina reads for sample quality
 using FastQC (Andrews 2010). We used STACKS v1.48 (Catchen et al. 2013)
 for initial sequence data processing such as demultiplexing samples, steps
 to rescue barcodes with at most one mismatch, clean data, and remove any
 read with an uncalled base, and truncate all reads to 95 bases (due to
 variable-length sequences from using both 100-bp and 150-bp sequencing
 platforms). Prior to analysis, we concatenated sequencing data for the
 same individual sequenced on different lanes. Then, we used a
 reference-based approach in the ipyrad v.0.9.42 (Eaton & Overcast
 2020) program to conduct individual-based analyses. Compared to de-novo
 assembly approaches, reference-based approaches have much lower error
 rates, higher accuracy, and less bias than do de-novo approaches (Rochette
 & Catchen 2017). We used the annotated <em>S.
 occidentalis</em> genome published by Harris et al. (2015) as the
 reference genome for the analysis. IPYRAD ANALYSIS
 PROTOCOL We followed the analysis recommendations and
 default settings specified by the program authors with some modifications
 specific to our datasets. We filtered and edited demultiplexed reads by
 removing reads with 5 or more low-quality base calls (Q&lt;20), and
 trimmed bases from the 3’ end of reads if their quality scores fell below
 20, which is 99% probability of a correct base call. Reads were then
 mapped to the reference genome using BWA (version 0.7.17-r1188) and
 clusters were aligned using MUSCLE (version 3.8.31), requiring a minimum
 depth of 6, which is the minimum depth at which a heterozygous base call
 can be distinguished from sequencing error. We jointly estimated
 heterozygosity and error rate by specifying a maximum of two alleles per
 site in each consensus sequence and removed alignments with a high
 proportion (5%) of heterozygous base calls, as poor alignments tend to
 have an excess of heterozygous sites. To remove poor alignments in
 repetitive regions in the final data set, we allowed for a maximum of 20%
 SNPs per locus, as well as removed alignments with more than 8 indels per
 locus. We set the maximum proportion of shared polymorphic sites in a
 locus to 0.5, as shared heterozygous sites across samples likely result
 from clustering of paralogs with fixed, rather than heterozygous sites,
 and excluded any samples with fewer than 100,000 reads. We retained all
 loci shared by 75% of samples in the dataset for analysis, as this
 provided a good balance between missingness and number of recovered SNPs
 in the analysis. Altogether for the landscape genetics analysis, we
 retained a sample of 49 individuals with 11,215 loci in the assembly and
 25.9% missing sites in the final SNP matrix. We output a SNP-based
 formatted file of one randomly selected variable site per locus to
 calculate individual-based pairwise genetic distances calculated as a
 proportion of shared alleles for individuals collected over the extent of
 our sampling range. For the phylogenetic dataset, we
 followed the same protocol as above for a sample of 200 <em>S.
 occidentalis</em> individuals that broadly encompass the Sierra
 Nevada range and one <em>S. graciosus</em> as an outgroup. We
 removed invariable sites from the alignment and retained all positions
 with data for at least 95% of the individuals (n=191) in the dataset. We
 obtained a dataset for the phylogenetic analysis with 27,872 variable
 sites in the final sequence matrix with 17.1% missing sites.