Convergence in maximum stomatal conductance of C3 woody angiosperms observed in natural ecosystems across six bioclimatic zones

  • Poster Presentation
  • Poster 20 (Flash Talk: 11 Jun 2018 17:09)
  • Foyer, UCD Agriculture and food science Centre
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Michelle Murray*
University College Dublin

Wuu Kuang Soh*
University College Dublin

Charilaos Yiotis
University College Dublin

Sven Batke
Edge Hill University

Andrew Parnell
University College Dublin

Robert Spicer
The Open University

Tracy Lawson
University of Essex

Rodrigo Cabellero
Stockholm University

Ian Wright
Macquarie University

Conor Purcell
University College Dublin

Jennifer C. McElwain
Trinity College Dublin

*Presenting Author

Stomata are at the interface between biosphere and atmosphere, regulating carbon uptake and water loss through stomatal conductance (gs) and intrinsically linking the carbon and water cycles. Yet, current understanding of the pattern of gs under natural field conditions across latitudinal gradients is limited. To determine the range and limits of gs across different bioclimatic zones and ecological groups and whether there is a central tendency in leaf-level maximum stomatal conductance (gsmax), we measured the daytime growing season gs in a wide range of C3 woody angiosperm species under prevailing environmental conditions in natural ecosystems. We present our analysis of 430 porometry-measured gsmax values derived from our novel ‘Straits’ (‘Stomatal Traits’) dataset of 4273 gs measurements, collected from 218 species in two broad ecological groups (open-canopy and understory-subcanopy) across 19 sites and seven bioclimatic zones. We observed convergence in gsmax across six bioclimatic zones in both ecological groups, which is stronger in the understory-subcanopy than in the open-canopy. Underlying this observation, we find a central tendency in gsmax in C3 woody angiosperms in natural ecosystems, which may be causally linked to maintaining optimum intercellular CO2 concentration (ci). STraits is an important new reference for predicting vegetation and ecosystem response to climate change and validation of climate model predictions.