Publication details
Leaf inclination angle and foliage clumping in an evergreen broadleaf Eucalyptus forest under elevated atmospheric CO2
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | Australian Journal of Botany |
| MU Faculty or unit | |
| Citation | |
| web | https://www.publish.csiro.au/BT/BT21035 |
| Doi | http://dx.doi.org/10.1071/BT21035 |
| Keywords | Eucalpytus woodland; EucFACE; foliage clumping; free-air CO2 enrichment; hemispherical photography; leaf angle distribution; leveled digital photography; optical canopy instrumentation |
| Description | How leaves are presented affects interaction of atmospheric CO2, energy (light), and plant physiology. Plant productivity is primarily determined by the amount of leaf area, leaf orientation and distribution in space. Not much attention has been paid to possible changes in leaf orientation and distribution with elevated CO2, but its effect on plant growth could alter the proportions of sunlit and shaded leaf areas and feedback on carbohydrate available for further growth. We report on first measurements of leaf inclination angle distribution and foliage clumping in a native evergreen Eucalyptus woodland in ambient CO2 and under +150 ppm elevated CO2. We found that a spherical leaf angle distribution was not an appropriate supposition for present species (Eucalyptus tereticornis Sm.) at this site. Our measurements of leaf inclination angles from imagery indicated an erectophile, highly skewed unimodal leaf inclination angle distribution function. We conclude that despite the measured steeper angles under elevated CO2 concentrations, the leaf angle change is not significant and falls within the expected natural variability and uncertainties connected with the measurement method. The lack of a clear response of leaf orientation and foliage clumping to elevated CO2 concentration indicates that the previously produced datasets of leaf inclination angles and foliage clumping maps with Earth observation data may be suitable while modelling carbon and water cycles under climate change. |