AbstractBackground and Aims
Optimal partitioning theory (OPT) predicts plants will allocate biomass to organs where resources are limiting. Studies of OPT focus on root, stem and leaf mass ratios where roots and stems are often further sub-divided into organs such as fine roots/tap roots or branches/main stem. Leaves, however, are rarely sub-divided into different organs. Heteroblastic species develop juvenile and adult foliage and provide an opportunity of sub-dividing leaf mass ratio into distinct organs. Acacia implexa (Mimosaceae) is a heteroblastic species that develops compound (juvenile), transitional and phyllode (adult) leaves that differ dramatically in form and function. The aims of the present study were to grow A. implexa to examine patterns of plastic development of whole-plant and leaf traits under the OPT framework.Methods
Plants were grown in a glasshouse under contrasting nutrient, light and water environments in a full factorial design. Allocation to whole-plant and leaf-level traits was measured and analysed with multivariate statistics.Key Results
Whole-plant traits strongly followed patterns predicted by OPT. Leaf-level traits showed a more complex pattern in response to experimental treatments. Compound leaves on low nutrient plants had significantly lower specific leaf area (SLA) and were retained for longer as quantified by a significantly greater compound leaf mass ratio after 120 d. There was no significant difference in SLA of compound leaves in the light treatment, yet transitional SLA was significantly higher under the low light treatment. The timing of heteroblastic shift from compound to transitional leaves was significantly delayed only in the low light treatment. Therefore, plants in the light treatment responded at the whole-plant level by adjusting allocation to productive compound leaves and at the leaf-level by adjusting SLA. There were no significant SLA differences in the water treatment despite strong trends at the whole-plant level.Conclusion
Explicitly sub-dividing leaves into different types provided greater insights into OPT.