Characterizing range-wide divergence in an alpine-endemic bird: a comparison of genetic and genomic approaches

Fine-scale adaptive divergence and population genetic structure of Aedes aegypti in Metropolitan Manila, Philippines

BACKGROUND

The adaptive divergence of Aedes aegypti populations to heterogeneous environments can be a driving force behind the recent expansion of their habitat distribution and outbreaks of dengue disease in urbanized areas. In this study, we investigated the population genomics of Ae. aegypti at a regional scale in Metropolitan Manila, Philippines.

METHODS

We used the Pool-Seq double digestion restriction-site association DNA sequencing (ddRAD-Seq) approach to generate a high number of single nucleotide polymorphisms (SNPs), with the aim to determine local adaptation and compare the population structure with 11 microsatellite markers. A total of 217 Ae. aegypti individuals from seven female and seven male populations collected from Metropolitan Manila were used in the assays.

RESULTS

We detected 65,473 SNPs across the populations, of which 76 were non-neutral SNPs. Of these non-neutral SNPs, the multivariate regression test associated 50 with eight landscape variables (e.g. open space, forest, etc.) and 29 with five climate variables (e.g. air temperature, humidity, etc.) (P-value range 0.005-0.045) in female and male populations separately. Male and female populations exhibited contrasting spatial divergence, with males exhibiting greater divergence than females, most likely reflecting the different dispersal abilities of male and female mosquitoes. In the comparative analysis of the same Ae. aegypti individuals, the pairwise FST values of 11 microsatellite markers were lower than those of the neutral SNPs, indicating that the neutral SNPs generated via pool ddRAD-Seq were more sensitive in terms of detecting genetic differences between populations at fine-spatial scales.

CONCLUSIONS

Overall, our study demonstrates the utility of pool ddRAD-Seq for examining genetic differences in Ae. aegypti populations in areas at fine-spatial scales that could inform vector control programs such as Wolbachia-infected mosquito mass-release programs. This in turn would provide information on mosquito population dispersal patterns and the potential barriers to mosquito movement within and around the release area. In addition, the potential of environmental adaptability observed in Ae. aegypti could help population control efforts.

Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing

BACKGROUND

Genome-wide data are invaluable to characterize differentiation and adaptation of natural populations. Reduced representation sequencing (RRS) subsamples a genome repeatedly across many individuals. However, RRS requires careful optimization and fine-tuning to deliver high marker density while being cost-efficient. The number of genomic fragments created through restriction enzyme digestion and the sequencing library setup must match to achieve sufficient sequencing coverage per locus. Here, we present a workflow based on published information and computational and experimental procedures to investigate and streamline the applicability of RRS.

RESULTS

In an iterative process genome size estimates, restriction enzymes and size selection windows were tested and scaled in six classes of Antarctic animals (Ostracoda, Malacostraca, Bivalvia, Asteroidea, Actinopterygii, Aves). Achieving high marker density would be expensive in amphipods, the malacostracan target taxon, due to the large genome size. We propose alternative approaches such as mitogenome or target capture sequencing for this group. Pilot libraries were sequenced for all other target taxa. Ostracods, bivalves, sea stars, and fish showed overall good coverage and marker numbers for downstream population genomic analyses. In contrast, the bird test library produced low coverage and few polymorphic loci, likely due to degraded DNA.

CONCLUSIONS

Prior testing and optimization are important to identify which groups are amenable for RRS and where alternative methods may currently offer better cost-benefit ratios. The steps outlined here are easy to follow for other non-model taxa with little genomic resources, thus stimulating efficient resource use for the many pressing research questions in molecular ecology.

Genetic structure of urban and non-urban populations differs between two common parid species

Landscape conversions induced by human activities can affect dispersal patterns of various bird species and, as a result, affect genetic structure of their populations. Genetic differentiation of bird populations may be enhanced by habitat variation, especially in urban-non-urban systems. The majority of population genetic studies focus on single species, which inflicts limitations for direct comparisons of genetic responses of avian populations to urbanization. Here, we used a set of microsatellite markers to examine genetic diversity, gene flow and population structure in two common parid species, great tits Parus major and blue tits Cyanistes caeruleus occupying three sites in habitats with contrasting urbanization level in central Poland. We found low but significant divergence of urban park population with both suburban and non-urban forest great tit populations, while no differentiation was found between suburban forest and non-urban forest populations. In contrast, no evidence for genetic differentiation was found between blue tit populations from the urban park, suburban forest and non-urban forest sites. We conclude that great tits and blue tits respond to urbanization-related changes in a different way, which may be a result of different rates of migration and/or dispersal, likely higher in blue tits. Some impact may be also induced by interspecific competition. We suggest that changing the focus of urban genetic research from single to multiple species may provide novel insights into how natural populations respond to the processes of urbanization.

Environmental gradients of selection for an alpine-obligate bird, the white-tailed ptarmigan (Lagopus leucura)

The warming climate will expose alpine species adapted to a highly seasonal, harsh environment to novel environmental conditions. A species can shift their distribution, acclimate, or adapt in response to a new climate. Alpine species have little suitable habitat to shift their distribution, and the limits of acclimation will likely be tested by climate change in the long-term. Adaptive genetic variation may provide the raw material for species to adapt to this changing environment. Here, we use a genomic approach to describe adaptive divergence in an alpine-obligate species, the white-tailed ptarmigan (Lagopus leucura), a species distributed from Alaska to New Mexico, across an environmentally variable geographic range. Previous work has identified genetic structure and morphological, behavioral, and physiological differences across the species' range; however, those studies were unable to determine the degree to which adaptive divergence is correlated with local variation in environmental conditions. We used a genome-wide dataset generated from 95 white-tailed ptarmigan distributed throughout the species' range and genotype-environment association analyses to identify the genetic signature and environmental drivers of local adaptation. We detected associations between multiple environmental gradients and candidate adaptive loci, suggesting ptarmigan populations may be locally adapted to the plant community composition, elevation, local climate, and to the seasonality of the environment. Overall, our results suggest there may be groups within the species' range with genetic variation that could be essential for adapting to a changing climate and helpful in guiding conservation action.

Phylogeography of the iconic Australian red-tailed black-cockatoo (Calyptorhynchus banksii) and implications for its conservation

Advances in sequencing technologies have revolutionized wildlife conservation genetics. Analysis of genomic data sets can provide high-resolution estimates of genetic structure, genetic diversity, gene flow, and evolutionary history. These data can be used to characterize conservation units and to effectively manage the genetic health of species in a broad evolutionary context. Here we utilize thousands of genome-wide single-nucleotide polymorphisms (SNPs) and mitochondrial DNA to provide the first genetic assessment of the Australian red-tailed black-cockatoo (Calyptorhynchus banksii), a widespread bird species comprising populations of varying conservation concern. We identified five evolutionarily significant units, which are estimated to have diverged during the Pleistocene. These units are only partially congruent with the existing morphology-based subspecies taxonomy. Genetic clusters inferred from mitochondrial DNA differed from those based on SNPs and were less resolved. Our study has a range of conservation and taxonomic implications for this species. In particular, we provide advice on the potential genetic rescue of the Endangered and restricted-range subspecies C. b. graptogyne, and propose that the western C. b. samueli population is diagnosable as a separate subspecies. The results of our study highlight the utility of considering the phylogeographic relationships inferred from genome-wide SNPs when characterizing conservation units and management priorities, which is particularly relevant as genomic data sets become increasingly accessible.

An empirical comparison of population genetic analyses using microsatellite and SNP data for a species of conservation concern

BACKGROUND

Use of genomic tools to characterize wildlife populations has increased in recent years. In the past, genetic characterization has been accomplished with more traditional genetic tools (e.g., microsatellites). The explosion of genomic methods and the subsequent creation of large SNP datasets has led to the promise of increased precision in population genetic parameter estimates and identification of demographically and evolutionarily independent groups, as well as questions about the future usefulness of the more traditional genetic tools. At present, few empirical comparisons of population genetic parameters and clustering analyses performed with microsatellites and SNPs have been conducted.

RESULTS

Here we used microsatellite and SNP data generated from Gunnison sage-grouse (Centrocercus minimus) samples to evaluate concordance of the results obtained from each dataset for common metrics of genetic diversity (HO, HE, FIS, AR) and differentiation (FST, GST, DJost). Additionally, we evaluated clustering of individuals using putatively neutral (SNPs and microsatellites), putatively adaptive, and a combined dataset of putatively neutral and adaptive loci. We took particular interest in the conservation implications of any differences. Generally, we found high concordance between microsatellites and SNPs for HE, FIS, AR, and all differentiation estimates. Although there was strong correlation between metrics from SNPs and microsatellites, the magnitude of the diversity and differentiation metrics were quite different in some cases. Clustering analyses also showed similar patterns, though SNP data was able to cluster individuals into more distinct groups. Importantly, clustering analyses with SNP data suggest strong demographic independence among the six distinct populations of Gunnison sage-grouse with some indication of evolutionary independence in two or three populations; a finding that was not revealed by microsatellite data.

CONCLUSION

We demonstrate that SNPs have three main advantages over microsatellites: more precise estimates of population-level diversity, higher power to identify groups in clustering methods, and the ability to consider local adaptation. This study adds to a growing body of work comparing the use of SNPs and microsatellites to evaluate genetic diversity and differentiation for a species of conservation concern with relatively high population structure and using the most common method of obtaining SNP genotypes for non-model organisms.