OOS 1 - Improving Tools for Ecosystem Prediction and Management Using Eco-evolutionary Optimality Theory

Organizer(s)

• MB

  Maoya Bassiouni

  UC Berkeley, United States

Co-Organizer(s)

• SH

  Sandy Harrison

  University of Reading
  Reading, United Kingdom
• EP

  Evan A Perkowski

  PhD candidate
  Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409, USA
  Lubbock, Texas, United States

Vegetation models, often embedded in land-surface and climate models, are currently used to predict how ecosystems will be affected by climate and other environmental changes. While these models are increasingly complex, major divergences in their predictions of future changes suggest that they do not capture plant physiological and ecological processes accurately. These uncertainties make it difficult to use future projections for develop well-founded strategies for ecosystem management. However, approaches based on eco-evolutionary optimality (EEO) theory are being used to develop simpler descriptions of key physiological and ecological processes, and how these change in response to changing environmental conditions both on physiological (hourly to seasonal) and ecological (years to decades) timescales. The incorporation of EEO-based approaches in land-surface models should provide a theoretically robust way of improving predictive capacity. The aim of this session is to explore recent progress in EEO-based modelling, and to demonstrate how these simple models are being used to understand and simulate vegetation responses to climate variability and to future changes in climate and environmental conditions, necessary to investigate and inform effective management strategies for mitigating the deleterious impacts of these changes.

Presentations:

• 3:30 PM – 3:45 PM PDT

  Water and vegetation: optimal adaptation, trade-offs and feedbacks
  

  Presenting Author: Stan Schymanski – Luxembourg Institute of Science and Technology
• 3:45 PM – 4:00 PM PDT

  Predicting the adaptive responses of biodiverse plant communities using functional trait evolution
  

  Presenting Author: Jaideep Joshi – University of Bern

  Co-author: Florian Hofhansl – International Institute for Applied Systems Analysis (IIASA)

  Co-author: Benjamin D. Stocker – University of Bern, Switzerland

  Co-author: Shipra Singh – Jawaharlal Nehru University, India

  Co-author: Åke Brännström – Umeå University, Sweden

  Co-author: Toyo Vignal – Mathematics, University of Edinburgh

  Co-author: Carolina C. Blanco – University of Campinas

  Co-author: Izabela Aleixo – National Institute of Amazonian Research, Brazil

  Co-author: David Lapola – University of Campinas, Brazil

  Co-author: Iain Colin C. Prentice – Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia

  Co-author: Ulf Dieckmann – OIST, Japan
• 4:00 PM – 4:15 PM PDT

  Growth optimization: a new framework for integrating stomata response with whole-plant hydraulics and carbon balance
  

  Presenting Author: Xue Feng – University of Minnesota, Twin Cities

  Co-author: Aaron Potkay – University of Minnesota
• 4:15 PM – 4:30 PM PDT

  Combining machine learning and eco-evolutionary theory to incorporate CO2 fertilization in global gross primary productivity upscaling
  

  Presenting Author: Yanghui Kang – UC Berkeley

  Co-author: Max Gaber – University of Copenhagen

  Co-author: Maoya Bassiouni – UC Berkeley

  Co-author: Xinchen Lu – University of California, Berkeley

  Co-author: Trevor Keenan – University of California, Berkeley
• 4:30 PM – 4:45 PM PDT

  Modelling the response of crops to future climate change using an EEO framework: a case study using wheat
  

  Presenting Author: Shengchao Qiao – Tsinghua University