EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Applying eco-evolutionary optimality principles to predict leaf area index

Wenjia Cai and Iain Colin Prentice
Wenjia Cai and Iain Colin Prentice
  • Georgina Mace Centre for the Living Planet, Department of Life Sciences, Imperial College London, London, United Kingdom

Leaf area index (LAI), defined as one-sided green leaf area per unit ground area, is an important biophysical property of terrestrial vegetation. As the primary locus of mass and energy exchange, leaf area is directly linked with carbon assimilation, evapotranspiration, and the energy and carbon balances of terrestrial ecosystems. Predicting the response of terrestrial vegetation under climate change requires accurate characterization of plant biophysical and biochemical processes in which LAI is a key determinant. Despite many successes, global vegetation and land surface models are still subject to systematic failures and divergences between model projections, indicating a need to develop and test more reliable representations of vegetation-climate interactions. LAI in particular is not well constrained by current models. Here we apply eco-evolutionary optimality (EEO) principles to derive a parsimonious approach to the prediction of LAI by balancing net carbon gain and water loss. Plants are expected to optimally allocate carbon to foliage for light capture and CO2 acquisition, until water losses via transpiration make further canopy development unsustainable. We hypothesize that LAI is limited by the minimum of two values determined by the energy supply for photosynthesis and the water supply by precipitation, respectively. With simple equations, requiring far fewer parameters than typical complex models, we demonstrate a gridded simulated annual maximum LAI that is broadly consistent with a similar measure derived from remotely sensed observations. Further development of this model over different time scales, and its incorporation into vegetation models, would be beneficial to achieve better carbon cycle projections in a changing world.

How to cite: Cai, W. and Prentice, I. C.: Applying eco-evolutionary optimality principles to predict leaf area index, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-995,, 2022.


Display file