Description
Understanding how populations respond to rapidly changing environments is crucial for informing conservation efforts, pest control, and climate-change mitigation strategies. The study of landscape genetic connectivity and adaptive genetic processes can disentangle the factors affecting species responses to changing environments. Insects that occupy variable environments are ideal for investigating this due to their short generation times and temperature sensitivity. Here I discuss findings from field-sampled genomic, morphological and environmental data collected from insects across contrasting environmental gradients. Using landscape resistance modelling with genotype x environment analyses, I present results from insects with contrasting demographic characteristics (i.e. patchy/range expanding, generalist/ widespread, and specialist/narrowly distributed). Common and unique drivers of neutral and adaptive genetic processes are identified, which partially addresses the challenge of making meaningful predictions about species’ evolutionary responses to environmental change.BIO: Rachael Dudaniec is a Senior Lecturer and leads the Landscape and Evolutionary Genetics Lab in the Department of Biological Sciences at Macquarie University. Her team applies field data collection and genomic approaches to explore how evolutionary processes are shaped by local environmental and morphological adaptation, genetic connectivity, introgression and hybridisation.This research is relevant for the conservation management of species, and assessing the adaptive capacity and spread risk for species with invasive or pest status, particularly under climate change. Study systems include pest grasshoppers and introduced bumblebees in Australia, range expanding damselflies in Europe, and host-parasite interactions for invasive parasitic flies of Darwin’s finches on the Galapagos Islands.
| Period | 30 Oct 2020 |
|---|---|
| Held at | The University of New South Wales, Australia, New South Wales |
| Degree of Recognition | National |