Identifying hybridization and admixture using SNPs:Application of the DArTseq platforminphylogeographic research on vertebrates

Next-generation sequencing (NGS) approaches are increasingly being used to generate multi-locus data for phylogeographic and evolutionary genetics research. We detail the applicability of a restriction enzyme-mediated genome complexity reduction approach with subsequent NGS (DArTseq) in vertebrate study systems at different evolutionary and geographical scales. We present two case studies using SNP data from the DArTseq molecular marker platform. First, we used DArTseq in a large phylogeographic study of the agamid lizard Ctenophorus caudicinctus, including 91 individuals and spanning the geographical range of this species across arid Australia. A low-density DArTseq assay resulted in 28 960 SNPs, with low density referring to a comparably reduced set of identified and sequenced markers as a cost-effective approach. Second, we applied this approach to an evolutionary genetics study of a classic frog hybrid zone (Litoria ewingii–Litoria paraewingi) across 93 individuals, which resulted in 48 117 and 67 060 SNPs for a low- and high-density assay, respectively. We provide a docker-based workflow to facilitate data preparation and analysis, then analyse SNP data using multiple methods including Bayesian model-based clustering and conditional likelihood approaches. Based on comparison of results from the DArTseq platform and traditional molecular approaches, we conclude that DArTseq can be used successfully in vertebrates and will be of particular interest to researchers working at the interface between population genetics and phylogenetics, exploring species boundaries, gene exchange and hybridization. © 2017 The Authors

Impacts of Traffic Noise and Traffic Volume on Birds of Roadside Habitats

Roadside habitats are important for a range of taxa including plants, insects, mammals, and birds, particularly in developed countries in which large expanses of native vegetation have been cleared for agriculture or urban development. Although roadside vegetation may provide suitable habitat for many species, resident animals can be exposed to high levels of traffic noise, visual disturbance from passing vehicles, and the risk of collision with cars and trucks. Traffic noise can reduce the distance over which acoustic signals such as song can be detected, an effect known as acoustic interference or masking. Studies from the northern hemisphere show that the singing behavior of birds changes in the presence of traffic noise. We investigated the impact of traffic noise and traffic volume on two species of birds, the Grey Shrike-thrush (Colluricincla harmonica) and the Grey Fantail (Rhipidura fuliginosa), at 58 roadside sites on the Mornington Peninsula, southeastern Australia. The lower singing Grey Shrike-thrush sang at a higher frequency in the presence of traffic noise, with a predicted increase in dominant frequency of 5.8 Hz/dB of traffic noise, and a total effect size of 209 Hz. In contrast, the higher singing Grey Fantail did not appear to change its song in traffic noise. The probability of detecting each species on a visit to a site declined substantially with increasing traffic noise and traffic volume, with several lines of evidence supporting a larger effect of traffic noise. Traffic noise could hamper detection of song by conspecifics, making it more difficult for birds to establish and maintain territories, attract mates and maintain pair bonds, and possibly leading to reduced breeding success in noisy roadside habitats. Closing key roads during the breeding season is a potential, but untested, management strategy to protect threatened bird species from traffic noise and collision with vehicles at the time of year when they are most vulnerable to their impacts. Other management options include reducing the speed and/or volume of traffic on such roads to an acceptably low level. Ours is the first study to investigate the effect of traffic noise on the singing behavior of birds in the southern hemisphere

Data from: Predicting the effect of urban noise on the active space of avian vocal signals

Urbanization changes the physical environment of non-human species, but also markedly changes their acoustic environment. Urban noise interferes with acoustic communication in a range of animals including birds, with potentially profound impacts on fitness. However, a mechanistic theory to predict which species of birds will be most affected by urban noise is lacking. We develop a mathematical model to predict the decrease in the active space of avian vocal signals when moving from quiet forest habitats to noisy urban habitats, and find that the magnitude of the decrease is largely a function of signal frequency. However, this relationship is not monotonic. A meta-regression of observed increases in the frequency of birdsong in urban noise supports the model's predictions for signals with frequencies between 1.5 and 4 kHz. Using the results of the meta-regression and the model described above, we show that the expected gain in active space following observed frequency shifts is up to 12%, and greatest for birds with signals at the lower end of this frequency range. Our generally-applicable model, along with three predictions regarding the behavioral and population-level responses of birds to urban noise, represents an important step towards a theory of acoustic communication in urban habitats

Interspecific variation in the phenology of advertisement calling in a temperate Australian frog community

Spatial and temporal partitioning of resources underlies the coexistence of species with similar niches. In communities of frogs and toads, the phenology of advertisement calling provides insights into temporal partitioning of reproductive effort and its implications for community dynamics. This study assessed the phenology of advertisement calling in an anuran community from Melbourne, in southern Australia. We collated data from 1432 surveys of 253 sites and used logistic regression to quantify seasonality in the nightly probability of calling and the influence of meteorological variables on this probability for six species of frogs. We found limited overlap in the predicted seasonal peaks of calling among these species. Those shown to have overlapping calling peaks are unlikely to be in direct competition, due to differences in larval ecology (Crinia signifera and Litoria ewingii) or differences in calling behavior and acoustics (Limnodynastes dumerilii and Litoria raniformis). In contrast, closely related and ecologically similar species (Crinia signfera and Crinia parinsignifera; Litoria ewingii and Litoria verreauxii) appear to have staggered seasonal peaks of calling. In combination with interspecific variation in the meteorological correlates of calling, these results may be indicative of temporal partitioning of reproductive activity to facilitate coexistence, as has been reported for tropical and temperate anurans from other parts of the globe

Path diagrams for the structural equation models fitted to species richness data.

<p>The saturated model (top) includes both direct effects of attenuated imperviousness (AI) and indirect effects through modification of aquatic vegetation. The mediation model only incorporates indirect effects of AI (centre), whereas the null model only includes direct effects for all predictors (bottom). DIC is the deviance information criterion and ∆DIC the difference between DIC scores of the best model and a given model. The dashed arrow represents the total indirect effect of attenuated imperviousness on species richness (by affecting aquatic vegetation). Solid arrows indicate direct effects. Numbers next to arrows indicate the standardized coefficient for that path (95% credible intervals in brackets).</p

Interaction between road density and aquatic vegetation in influencing species richness.

<p>Road density values log-transformed on the x-axis.</p

Marginal effect plots of the three main explanatory variables (deviance explained >5%).

<p>Plots represent the effect of each variable on species richness (top) and probability of occupancy for <i>C</i><i>. signifera</i> (centre) and <i>L</i><i>. dumerilii</i> (bottom) while controlling for the average effect of all other variables in the model. Road density values log-transformed. Dashed lines represent 95% confidence intervals calculated over 1,000 iterations of the model fitting function.</p

Predicted decline in communication distance in 6 species of birds; Data used in meta-regression of frequency shifts in urban noise

Data collated from the published literature, file created using MS Word. Column headings described in data file. Sources: Hu & Cardoso (2009) Behavioral Ecology 20:1268-1273; Brackenbury (1979) Journal of Experimental Biology 78:163-166; Calder (1990) Ecology 71:1810-1816; Wood & Yezerinac (2006) The Auk 123:650-659; Parris & Schneider (2009) Ecology and Society 14(1):29; Fernandez-Juricic et al. (2005) Urban Habitats 3:49-69; Gross et al. (2010) American Naturalist 176:456-464; Potvin et al. (2011) Proceedings of the Royal Society B 278:2464-2469; Mockford & Marshall (2009) Proceedings of the Royal Society B 276:2979-2985; Slabbekoorn & Peet (2003) Nature 424:267; Slabbekoorn & den Boer-Visser (2006) Current Biology 16:2326-2331

Map of the study area.

<p>Circles indicate the location of survey transects. The grey area represents the extent of current development, with the dark grey rectangle indicating the central business district of the city. Waterways in black.</p

Appendix B. Descriptive statistics for eight continuous variables recorded at 65 ponds in Greater Melbourne, Australia.

Descriptive statistics for eight continuous variables recorded at 65 ponds in Greater Melbourne, Australia