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An ecologist by training – I hold a B.Sc. (Hons) in Marine Ecology from the University of North Carolina, Wilmington and a Ph.D. in Ecological Modelling from Griffith University. I am broadly interested in exploring new ways to (1) understand how natural communities are formed and (2) predict how they will change over time. As an Amplify Fellow at UQ, my current research focuses on developing computational tools and adapting techniques from epidemiology and statistical forecasting to study how organisms and ecosystems respond to environmental change. This work is being applied to investigate natural dynamics for a range of natural systems including host-parasite interactions, wildlife populations and veterinary diseases.

I am an active member of the R community and have written and/or maintain several popular R packages. For example, I’m a lead developer on the MRFcov package for multivariate conditional random fields analyses. I also wrote the mvgam R package for fitting dynamic Generalised Additive Models to analyse and forecast multivariate ecological time series, and I regularly provide training seminars and workshops to help researchers learn techniques in ecological data analysis.

I am currently seeking Honours and PhD candidates with interests and/or skills in veterinary epidemiology, spatial / spatiotemporal modeling and quantitative ecology.

Paul Dennis leads an exciting research group that applies cutting-edge technologies to understand the roles of microorganisms and their responses to environmental change.

He is also a passionate educator and public speaker who advocates for the importance of biological diversity and evidence-based environmental awareness. He has talked about his research on ABC Radio and a range of other media outlets.

His teaching covers aspects of ecology, microbiology, plant and soil science, and climatology. He considers these topics to be of fundamental importance for the development of more sustainable societies and takes pride in helping others to obtain the knowledge and skills they need to build a better future.

Paul's research has taken him to Antarctica, the Amazon Rainforest, high mountains and oceans. The approaches used in his lab draw on a wide range of expertise in molecular biology, ecology, statistics, computer science, advanced imaging and soil science. He applies these skills to a wide-range of topics and systems including plant-microbe interactions, Antarctic marine and terrestrial ecology, biogeography, pollution and human health.

Bio

Dr. Yang Liu is an evolutionary geneticist, currently working at the University of Queensland (UQ) as a Research Fellow. Prior to UQ, he obtained a PhD from the University of British Columbia (UBC) and did a postdoc research at UBC and University of Cambridge. He is broadly interested in the eco-evolutionary dynamics of plant populations that have undergone environmental heterogeneity over spatiotemporal scales. The goal of his research is to increase our understanding of the impacts of major episodes in plant demography and life histories on trait evolution and to foster sustainability. He tackles research questions at the interface between ecology and evolutionary biology with the integration of population genetics and quantitative genomics to elucidate the ecological and genetic basis of phenotypic traits and biological adaptation.

Currently, he leverages available Arabidopsis natural accessions across its geographic distribution range, coupled with their genomic data, to perform common-garden and divergent selection experiments. From these he aims to dissect features of the genetic architecture of traits and to reveal their relationships to environmental conditions. He is focusing on the shoot branching phenotype and its associated traits including flowering timing.

ECO-EVO-GENOMICS TEAM

Ongoing Projects

Three PhD positions available in 2023-2025

Project 1: Unification of selection and inheritance informs adaptive potential for generations to come (Applications open in 2023; CLOSED)

Natural selection acts on phenotypes and produces immediate phenotypic effects within a generation. In this short-term process, some phenotypes are more successful than others. Use of single traits for selection analysis could generate opposing outcomes and cannot predict how selection operates on an organism. In contrast, multivariate selection in trait combinations utilizes the attribute of functional integrations to reveal how selection works in a multi-dimensional trait space. Selection is an important force driving evolution but not equal to evolution; the latter leads to changes in genetic variation. Only through assessment of the evolutionary responses of phenotypes can we understand the transmission of such selection from one generation to the next. How does selection occurring within a generation affect evolution across generations? In the project, we aim to address the question by unifying the two processes to forecast evolutionary potential in relation to selection. To that end, we partition genetic variance into components based on an experimental design, employ experimental evolution to estimate additive genetic variance-covariances (G) on quantitative scales and evaluate G-matrix evolution. We eventually hope to elucidate how populations subjected to artificial selection move along evolutionary trajectories and whether there are genetic constraints making the fitness optimum evolutionarily inaccessible.

Project 2: Genetic and ecological bases of shoot branching divergence across Arabidopsis species-wide accessions (Applications open in 2024)

Spatial patterns of genetic variation are shaped by environmental factors, topological features, and dispersal barriers. As a result, we often can identify population genetic structure stratified by geographic locations or ecological niches, the drivers of population isolation by distance or the environment, clinal genetic variation over space in alignment with gradually varying environment gradients, and adaptive genetic variation in relation to environmental variables. At the ecological level, assembly rules uncover the coordination of phenotypic traits along environmental clines. Tradeoffs between traits represent the consequence of environmental filters and reflect adaptation to environmental heterogeneity. For example, three fundamental adaptive strategies are delineated by a CSR theory, that is, Competitors, Stress-tolerators, and Ruderals. As such, ways of genetic and phenotypic assemblage over space and throughout time point to a role for natural selection driven by spatially varying environmental conditions to maintain genetic variation that confers natural variation in phenotypes. In this project, we focus on an important agronomic trait – shoot branching – due to its important contribution to the overall shoot architecture of a plant and being a potential target for yield optimization. We aim to dissect features of the genetic architecture of the trait and to reveal its relationships to environmental conditions. We integrate geographic, environmental, and genomic data from the 1001 Arabidopsis Genomes Project, coupled with the branching phenotype measured in selected accessions and then forecasted for the rest of the 1001 accessions using machine-learning models, to investigate the ecological relevance and genetic underpinnings of branching divergence across the Arabidopsis species-wide accessions. Our study has implications for enhancing our understanding of the genetic and ecological basis of shoot branching divergence and the potential for generating novel knowledge for improving phenotypic predictability.

Project 3: Dimensionality, modularity, and integration: Insights from the architecture features of pan-genomes, pan-transcriptome, pan-epigenomes, and pan-chromatin (applications open in 2025) Application Portal ALSO ACCEPTING EXPRESSION OF INTEREST FROM INTERNATIONAL APPLICANTS

Organisms are functionally integrated systems, where interactions among phenotypic traits make the whole more than the sum of its parts. How is a suite of traits assembled into an adaptive module? How is an intramodule rewired to form a regulatory network? What is the persistence and stability of a module under exposures to perturbations triggered by altered interactions between the response to disparate environmental conditions or between the responses of multiple traits to the same environment? What constrains modules to vary independently, reflecting the integration and canalization of evolutionary trajectories? In this project, we utilize a compilation of pan-genomes, pan-transcriptome, pan-epigenomes, and pan-chromatin resources of Arabidopsis thaliana to uncover how dimensionality, modularity, and integration are organized at different omics levels including genetic polymorphisms, structural variants, RNA isoforms, expression abundance, epigenetic imprinting, and chromatin accessibility. Ultimately, we apply such functional elements to multivariate genomic selection, in the hope of enhancing multilayered omics-enabled prediction.

I have a strong interest in applied research, using information to improve policy. I have a broad interest in applying population genetics to the management of wild populations, particularly through a better understanding of dispersal.

I have a keen interest in the evolutionary relationships that underpin symbioses, particularly those involved in plant disease. There are countless examples of how diseases have impacted on different crops throughout history, and this is an ongoing issue that deleteriously impacts food security. My research involves developing a better understanding of the epidemiology of plant diseases and pests, and delivering improved diagnostics and field management. Working with collaborators and international experts, my work involves research on a broad range of plants that are affected by bacteria, fungi, oomycetes and viruses. I have a strong interest in the biotic factors that govern soil health and the methods by which we can promote the development of beneficial microbial communities.