The Genomic Landscapes of Desert Birds Form over Multiple Time Scales

Abstract

Spatial models show that genetic differentiation between populations can be explained by factors ranging from geographic distance to environmental resistance across the landscape. However, genomes exhibit a landscape of differentiation, which could indicate that multiple spatial models better explain divergence in different portions of the genome. We test whether alternative geographic predictors of intraspecific differentiation vary across the genome in ten bird species that co-occur in Sonoran and Chihuahuan Deserts of North America. Using population-level genomic data, we characterized the genomic landscapes across species and modeled five predictors that represented historical and contemporary mechanisms. The characteristics of genomic landscapes differed across the ten species, influenced by varying levels of population structuring and admixture between deserts. General dissimilarity matrix modeling indicated that the best-fit models differed from the whole genome and partitions along the genome. Almost all of the historical and contemporary mechanisms were important in explaining genetic distance, but particularly historical and contemporary environment, while contemporary abundance, position of the barrier to gene flow, and distance explained relatively less. Individual species have significantly different patterns of genomic variation. These results illustrate that the genomic landscape of differentiation was influenced by alternative geographic factors operating on different portions of the genome.

Mitochondrial Genome Analysis and Phylogeny and Divergence Time Evaluation of theStrix aluco

“…This has led to species differentiation (Leonard et al, 2015). Glaciation has played an important role in influencing the population size, species and community genetic structure of today’s species (Svendsen et al, 2004; Hewitt 2000, 2004), the glacial-interglacial gyrations of the same period also affected the distribution of species (Kozma et al, 2018; Zhao et al, 2013; Hung et al, 2014; Mays Jr et al, 2018), Glacial-interglacial cycles led to periodic shifts in glacial refuges for Pleistocene birds (Nadachowska-Brzyska et al, 2015), the isolation of glacier refugia will lead to the divergence of the whole genome of species, thus forming different species (Provost et al, 2022), this should be the reason why the common ancestor of S. aluco MN122823 and S. aluco OP850567 (this study) diverged at 1.59~2.51Ma, genetic divergence of the same lineage due to the isolation of refugees leads to lineage divergence. All kinds of species generally begin to migrate to the best habitat during the warm climate period (Claramunt and Cracraft, 2015), in particular, species adapted to low altitudes in the early stage of climate change will move to high altitudes at this time, resulting in the reproductive isolation of species in the two places (Wiens, 2004).…”

“…Glaciation has played an important role in influencing the population size, species and community genetic structure of today's species (Svendsen et al, 2004;Hewitt 2000Hewitt , 2004, the glacial-interglacial gyrations of the same period also affected the distribution of species (Kozma et al, 2018;Zhao et al, 2013;Hung et al, 2014;Mays Jr et al, 2018), Glacial-interglacial cycles led to periodic shifts in glacial refuges for Pleistocene birds (Nadachowska-Brzyska et al, 2015), the isolation of glacier refugia will lead to the divergence of the whole genome of species, thus forming different species (Provost et al, 2022), this should be the reason why the common ancestor of S.…”

RecView: an interactive R application for viewing and locating recombination positions using pedigree data

“…The recombination landscape with regions of high and low recombination rates can be quantified by estimating genetic distances between markers segregating in large pedigrees (Groenen et al ., 2009; Johnston et al ., 2017) and by using population-level estimates of LD (Provost et al ., 2022; Singhal et al ., 2015). Another approach to study recombination is to locate actual recombination positions on individual chromosomes originating from single meiotic events (Bell et al ., 2020; Smeds et al ., 2016).…”

“…Furthermore, the recombination rate can be estimated by comparing genetic linkage maps to physical maps (Dumont et al ., 2011; Groenen et al ., 2009; Johnston et al ., 2017; Robinson, 1996). Likewise, recombination rates can be estimated by quantifying the degree of LD along chromosomes, with the rationale that high recombination rates lead to faster LD decay (Mackay and Powell, 2007; Provost et al ., 2022; Singhal et al ., 2015). Both linkage maps and LD patterns provide estimates of the population average rate of recombination over the chromosomes.…”

Male-biased recombination at chromosome ends in a songbird revealed by precisely mapping crossover positions

“…The advent of next-generation sequencing (NGS) enabled the screening of dense marker sets in many individuals, facilitating pedigree-free methods to study recombination rates. These methods involve assessing the local level of linkage disequilibrium (LD) and assuming a negative correlation between LD and recombination rate (Singhal et al 2015; Provost et al 2022). This has been accompanied by progress in statistics and model inferences of the population-level recombination landscape (McVean and Auton 2007; Chan et al 2012; Gao et al 2016; Adrion et al 2020).…”