Niche 2.0 - Can we predict vegetation structure, diversity & function from ecological and evolutionary first principles?
IAE Seminar Series

  • Faculty of Education, Maths, Technology and Science
  • Environmental Science
  • Environment
  • Institute for Applied Ecology
  • Public lectures/seminars
  • Staff Events
  • Student Events
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Walking through any forest, one is struck by the variety of plant forms coexisting. To explain vegetation structure and diversity, models must allow for multiple species to coexist, and ultimately, predict the outcome of community assembly in different environments. In this talk Daniel will outline a new framework for predicting the mixtures of species traits that are favoured in vegetation and evaluate the challenges in scaling these predictions to the Australian continent.

Additional Information

Predictions are generated by embedding trait-based coexistence and selection into models of forest dynamics, mapping from physiological trade-offs in plant function to individual-level outcomes such as growth rates, population demographics, and fitness. Results thus far show how i) how key trait-based trade-offs enable different strategies to coexist via successional niche differentiation; ii) how joint consideration of multiple traits can produce forests of higher diversity than was previously thought possible; and iii) how trait mixtures respond to environmental conditions. Current major challenges include expanding the variety of niches accounted for, parameterising models via open data, competition for multiple resources, and amassing trait data to road-test predictions. About Daniel: Dr Daniel Falster is an ARC Future Fellow at the University of New South Wales in Sydney, Australia. He uses a combination of maths, computer models, and large data sets to test fundamental ideas about the processes shaping forests. His current focus is on predicting the distribution of plant types found across the Australian continent. He is passionate about science, open data, reproducible research, and teaching biologists to code.