Initiation of speciation across multiple dimensions in a rock‐restricted, tropical lizard

Abstract

Population isolation and concomitant genetic divergence, resulting in strong phylogeographical structure, is a core aspect of speciation initiation. If and how speciation then proceeds and ultimately completes depends on multiple factors that mediate reproductive isolation, including divergence in genomes, ecology and mating traits.Here we explored these multiple dimensions in two young (Plio-Pleistocene) species complexes of gekkonid lizards (Heteronotia) from the Kimberley-Victoria River regions of tropical Australia. Using mitochondrial DNA screening and exon capture phylogenomics, we show that the rock-restricted Heteronotia planiceps exhibits exceptional fine-scale phylogeographical structure compared to the codistributed habitat generalist Heteronotia binoei. This indicates pervasive population isolation and persistence in the rock-specialist, and thus a high rate of speciation initiation across this geographically complex region, with levels of genomic divergence spanning the "grey zone" of speciation. Proximal lineages of H. planiceps were often separated by different rock substrates, suggesting a potential role for ecological isolation; however, phylogenetic incongruence and historical introgression were inferred between one such pair.Ecomorphological divergence among lineages within both H. planiceps and H. binoei was limited, except that limestone-restricted lineages of H. planiceps tended to be larger than rock-generalists. By contrast, among-lineage divergence in the chemical composition of epidermal pore secretions (putative mating trait) exceeded ecomorphology in both complexes, but with less trait overlap among lineages in H. planiceps.This system-particularly the rock-specialist H. planiceps-highlights the role of multidimensional divergence during incipient speciation, with divergence in genomes, ecomorphology and chemical signals all at play at very fine spatial scales.

The effect of missing data on evolutionary analysis of sequence capture bycatch, with application to an agricultural pest

“…Developments in bioinformatic software have further enabled utilization of bycatch data, for example to detect copy number variation (Kuilman et al 2015 ; Laver et al 2022 ) from unmapped DNA and RNA reads (Zhang et al 2016 ; Gasc et al 2016 ; Laine et al 2019 )—including from public data (Vieira and Prosdocimi 2019 ). Collectively, this work demonstrates the value (and quality; Guo et al 2012 ) of sequence data derived from outside targeted regions, and its use for examining a variety of evolutionary questions is growing (e.g., Derkarabetian et al 2019 ; Ballesteros et al 2020 ; Reilly et al 2022 ; Zozaya et al 2022 ). However, while the effects of missing data in studies employing phylogenetic inference have been examined (both generally, and in the context of sequence capture; see Tilston Smith et al 2020 , and references therein), its effects on population genetic and phylodynamic analyses—particularly when the data is bycatch and therefore more likely to be patchy in nature—have received less focus.…”

“…For example, in previous human research, high-quality SNPs from outside target regions bolstered tested datasets by up to 461% (Guo et al 2012 ). Indeed, this is a growing field (e.g., Derkarabetian et al 2019 ; Ballesteros et al 2020 ; Granados Mendoza et al 2020 ; Sanderson et al 2020 ; Costa et al 2021 ; Reilly et al 2022 ; Zozaya et al 2022 ), and we recommend that more researchers consider the extraction and analysis of bycatch data (as well as other off-target genomic resources, such as unmapped RNA reads in transcriptomic studies), in their informatics pipelines. Although some of these data will undoubtedly represent contamination and/or poor quality sequences, what remains may provide the raw material for new avenues of active research (Samuels et al 2013 ; Griffin et al 2014 ; Seaby et al 2016 ).…”

“…Targeted capture, in which selected regions of the genome are sequenced following enrichment from a whole genomic DNA extract, produces sequence data that can be used to address a range of fundamental and applied biological questions (Jones and Good 2016 ), including medical (e.g., detecting disease variants; Coutelier et al 2018 ; Nagy-Szakal et al 2021 ) and eco-evolutionary (Jones and Good 2016 ). In the latter case, the resulting large multi-locus datasets are often used for phylogenomic experiments (Andermann et al 2020 ; Ballesteros et al 2020 ; Reilly et al 2022 ; Zozaya et al 2022 ), while the enrichment step makes targeted capture suitable for working with historical and ancient DNA specimens—where the available DNA is present in small amounts and in a highly degraded state (Bi et al 2013 ; Derkarabetian et al 2019 ; Roycroft et al 2022 ).…”