Exploring the drivers of population structure across desert snakes can help to link micro and macroevolution

A novel method for identifying key genes in macroevolution based on deep learning with attention mechanism

Macroevolution can be regarded as the result of evolutionary changes of synergistically acting genes. Unfortunately, the importance of these genes in macroevolution is difficult to assess and hence the identification of macroevolutionary key genes is a major challenge in evolutionary biology. In this study, we designed various word embedding libraries of natural language processing (NLP) considering the multiple mechanisms of evolutionary genomics. A novel method (IKGM) based on three types of attention mechanisms (domain attention, kmer attention and fused attention) were proposed to calculate the weights of different genes in macroevolution. Taking 34 species of diurnal butterflies and nocturnal moths in Lepidoptera as an example, we identified a few of key genes with high weights, which annotated to the functions of circadian rhythms, sensory organs, as well as behavioral habits etc. This study not only provides a novel method to identify the key genes of macroevolution at the genomic level, but also helps us to understand the microevolution mechanisms of diurnal butterflies and nocturnal moths in Lepidoptera.

There Is No 'Rule of Thumb': Genomic Filter Settings for a Small Plant Population to Obtain Unbiased Gene Flow Estimates

The application of high-density polymorphic single-nucleotide polymorphisms (SNP) markers derived from high-throughput sequencing methods has heralded plenty of biological questions about the linkages of processes operating at micro- and macroevolutionary scales. However, the effects of SNP filtering practices on population genetic inference have received much less attention. By performing sensitivity analyses, we empirically investigated how decisions about the percentage of missing data (MD) and the minor allele frequency (MAF) set in bioinformatic processing of genomic data affect direct (i.e., parentage analysis) and indirect (i.e., fine-scale spatial genetic structure - SGS) gene flow estimates. We focus specifically on these manifestations in small plant populations, and particularly, in the rare tropical plant species Dinizia jueirana-facao, where assumptions implicit to analytical procedures for accurate estimates of gene flow may not hold. Avoiding biases in dispersal estimates are essential given this species is facing extinction risks due to habitat loss, and so we also investigate the effects of forest fragmentation on the accuracy of dispersal estimates under different filtering criteria by testing for recent decrease in the scale of gene flow. Our sensitivity analyses demonstrate that gene flow estimates are robust to different setting of MAF (0.05-0.35) and MD (0-20%). Comparing the direct and indirect estimates of dispersal, we find that contemporary estimates of gene dispersal distance (σ _{r t} = 41.8 m) was ∼ fourfold smaller than the historical estimates, supporting the hypothesis of a temporal shift in the scale of gene flow in D. jueirana-facao, which is consistent with predictions based on recent, dramatic forest fragmentation process. While we identified settings for filtering genomic data to avoid biases in gene flow estimates, we stress that there is no 'rule of thumb' for bioinformatic filtering and that relying on default program settings is not advisable. Instead, we suggest that the approach implemented here be applied independently in each separate empirical study to confirm appropriate settings to obtain unbiased population genetics estimates.

Predictors of phylogeographic structure among codistributed taxa across the complex Australian monsoonal tropics

Differences in the geographic scale and depth of phylogeographic structure across codistributed taxa can reveal how microevolutionary processes such as population isolation and persistence drive diversification. In turn, environmental heterogeneity, species' traits, and historical biogeographic barriers may influence the potential for isolation and persistence. Using extensive SNP data and a combination of population genetic summary statistics and landscape genomic analyses, we explored predictors of the scale and depth of phylogeographic structure in codistributed lizard taxa from the topographically and climatically complex monsoonal tropics (AMT) of Australia. We first resolved intraspecific lineages and then tested whether genetic divergence across space within lineages is related to isolation by distance, resistance and/or environment and whether these factors differ across genera or between rock-related versus habitat generalist taxa. We then tested whether microevolutionary processes within lineages explain differences in the geographic scale and depth of intraspecific phylogeographic lineages. The results indicated that landscape predictors of phylogeographic structure differ between taxa. Within lineages, there was prevalent isolation by distance, but the strength of isolation by distance is independent of the taxonomic family, habitat specialization, and climate. Isolation by environment is the strongest predictor of landscape-scale genetic divergence for all taxa, with both temperature and precipitation acting as limiting factors. The strength of isolation by distance does not predict the geographic scale of the phylogeographic structure. However, more localized lineages had higher mean individual heterozygosity and less negative Tajima's D. This result implies that finer-scale phylogeographic structuring within species is associated with larger and more stable populations and, hence, persistence.