Biology subjects

Gruber, B.

Publications and source records attributed to Gruber, B..

3 recordsLinked to original sources

bioRxiv2022

Contrasting reproductive strategies despite convergent traits for two dryland river-floodplain species

Aquatic plants share a range of convergent reproductive strategies, such as the ability to reproduce both sexually and asexually through vegetative growth. In dryland river systems, floodplain inundation is infrequent and irregular, and wetlands consist of discrete and unstable habitat patches. In these systems life-history strategies such as long-distance dispersal, seed longevity, self-fertilisation, and reproduction from vegetative propagules are important strategies which allow plants to persist. Using two aquatic plants Marsilea drummondii and Eleocharis acuta, we investigated the proportions of sexual and asexual reproduction and self-fertilisation employing next generation sequencing approaches and used this information to understand population genetic structure in a large inland floodplain, in western New South Wales (NSW) Australia. Asexual vegetative reproduction and self-fertilisation was more common in M. drummondii, but both species used sexual reproduction as the main mode of reproduction. This resulted in highly differentiated genetic structure between wetlands and similar genetic structure within wetlands. The similarity in genetic structure was influenced by the wetland in the two species highlighting the influence of the floodplain landscape and hydrology in structuring population genetic structure. The high levels of genetic variation among wetlands and low variation within wetlands suggests that dispersal and pollination occur within close proximity and that gene flow is restricted. This suggests a reliance on locally sourced (persistent) seed, rather than asexual (clonal) reproduction or recolonisation via dispersal, for population maintenance in plants in dryland rivers. This highlights the importance of floodplain inundation to promote seed germination, establishment and reproduction in dryland regions.

plant biology↗

bioRxiv2022

dartR v2: an accessible genetic analysis platform for conservation, ecology, and agriculture

O_LIInnumerable approaches to analyse genetic data are now available to guide conservation, ecological and agricultural projects. However, streamlined and accessible tools are needed to bring these approaches within reach of a broader user base. dartR was released in 2018 to lessen the intrinsic complexity of analysing single nucleotide polymorphisms (SNPs) and dominant markers (presence/absence of amplified sequence tags) by providing user-friendly data quality control and marker selection functions. dartR users have grown steadily since its release and provided valuable feedback on their interaction with the package allowing us to enhance dartR capabilities. C_LIO_LIHere, we present Version 2 of dartR. In this iteration, we substantially increased the number of available functions from 45 to 144. In addition to improved functionality, we focused on enhancing the user experience by extending plot customisation, function standardisation, increasing user support and function speed. dartR provides functions for various stages in analysing genetic data, from data manipulation to reporting. C_LIO_LIdartR provides many functions for importing, exporting and linking to other packages, to provide an easy-to-navigate conduit between data generation and analysis options already available via other packages. We also implemented simulation functions whose results can be analysed seamlessly with several other dartR functions. C_LIO_LIAs more methods and approaches mature to inform conservation, we envision that accessible platforms to analyse genetic data will play a crucial role in translating science into practice. C_LI

genomics↗

bioRxiv2021

slimr: An R package for integrating data and tailor-made population genomic simulations over space and time

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between simulation software and software designed for working with empirical data is currently not well developed. Here we present slimr, an R package designed to create a seamless link between standalone software SLiM 3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning, and manipulation, proceed to constructing empirically-based parameters and initial conditions for simulations, then to running numerical simulations, and finally to retrieving simulation results in a format suitable for comparisons with empirical data - aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SliM 3.0 directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes.

evolutionary biology↗