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Department of Biological Sciences - Westoby Lab

Evolutionary Ecology of Vegetation

Our main research aim is to understand and predict from evolutionary principles the physiognomy and traits of vegetation and why they vary round the world. Vegetation provides the physical habitat and the chemical flow of energy and cycling of nutrients within which other terrestrial life-forms operate. It also strongly influences hydrological cycles and exchange of gases with the atmosphere. The high-CO2 atmosphere that now seems likely will present opportunities for novel plant ecological strategies, leading to shifts in allocation and outcomes.

Currently research in the lab falls under the following broad headings.

Quantifying trait variation and trade-offs

One strand of research targets stem and leaf tissues, collecting field data across a wide range of species with respect to hydraulic function and biomechanical strength. The focus on hydraulics complements previous research on leaf economics and on seed size and life history.

Key people: Sean Gleason, Wade Tozer, Chris Blackman, Andrea Stephens, Yvonne Chang, Julieta Rosell, Mark Westoby.

Collaborators: Ian Wright, Amy Zanne, Peter Reich.

Linking traits to demographic rates

Another strand of research seeks to understand how traits influence plant growth rate. Growth rates are being recorded at a number of sites across eastern Australian, spanning gradients of rainfall and temperature. We are also developing theory that describes how the influence of traits on growth would be expected to change with plant size and environment.

Key people: Sean Gleason, Wade Tozer, Andrea Stephens, Yvonne Chang, Julieta Rosell, Steph Stuart, Elizabteh Wenk, Daniel Falster, Geogres Kunstler, Mark Westoby.

Collaborators: Ian Wright.

Theory predicting how traits should vary across the landscape

A third strand is developing theory predicting what sorts of plants grow where and why. The ultimate goal is to predict the traits of vegetation, the key feature missing from current global vegetation models.

Models are built on the principle that natural selection relentlessly favours ecological strategies with greater fitness. By incorporating natural selection into models of vegetation, we can evolve the trait mixtures in silico, and compare the predicted vegetation against that found in real forests.

During his PhD, Daniel Falster showed how physiology, ecology and evolution could be integrated in a model capable of predicting trait distributions. This work, done in collaboration with Ulf Dieckmann and Åke Brännström from IIASA (Austria), provided a much-needed theoretical framework for evolving traits mixtures.

Our current work seeks to

  1. Extend the existing model to include other traits, and predict how traits mixtures vary across environmental gradients.
  2. Compare model predictions against real-world data.

In additional to our existing collaborations, we are excited to be collaborating with Sydney-based researchers from NICTA and the software company SIRCA, to extend this research by applying machine learning techniques to the output of our evolutionary models. This will greatly accelerating the scale over which we can make testable predictions.

Key people: Daniel Falster, Rich FitzJohn, Georges Kunstler, Mark Westoby, James Camac.

Funding sources

Currently our research is funded by:

  • an Australian laureate fellowship awarded to Mark Westoby from the Australian Research Council (2010-2015): "Evolutionary ecology of vegetation".
  • an Australian post-doctoral fellowship awarded to Daniel Falster from the Australian Research Council (2011-2014): "Putting adaptation into vegetation models: towards a predictive theory of trait diversity and stand structure".
  • a Marie Curie grant awarded to Georges Kunstler (2012-2015): "DEMO-TRAITS: Tree demography, functional traits and climate change".
  • a post-doctoral fellowship awarded to Steve van Sluyter from the Science Industry Endowment Fund (2013 - 2015).
  • a project grant awarded to Mark Westoby and Daniel Falster (along with NICTA, SIRCA, and research groups in laser physics and in continental plate movements) from the Science Industry Endowment Fund (2013-2016): "Big Data Knowledge Discovery: Machine Learning meets Natural Science".


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