DESPOT, a process-based tree growth model that allocates carbon to maximize carbon gain.

Area of Science:

• Ecology
• Plant Physiology
• Computational Biology

Background:

• Understanding tree growth dynamics is crucial for forest management and ecological modeling.
• Existing models often simplify carbon allocation strategies, limiting their predictive power.
• Process-based modeling offers a more mechanistic approach to simulating complex biological systems.

Purpose of the Study:

• To introduce DESPOT (Deducing Emergent Structure and Physiology Of Trees), a novel process-based model for tree growth.
• To investigate carbon allocation strategies that maximize whole-tree net carbon gain.
• To evaluate the model's behavior and sensitivity using parameter analysis.

Main Methods:

• Developed DESPOT, a model simulating carbon gain, respiration, and resource acquisition (nitrogen, water, light) on a process basis.
• Modeled a uniform, monospecific, self-thinning forest stand.
• Employed Monte Carlo parameter sensitivity analysis to assess model predictions.

Main Results:

• DESPOT predicts determinate height growth and stable stand-level net primary productivity (NPP).
• The model indicates slow declines in aboveground NPP and leaf area index over time.
• Sensitivity analysis revealed that many parameter sets do not yield sustainable NPP.

Conclusions:

• Sustainable tree growth in the DESPOT model is contingent on specific parameter sets.
• Mature trees exhibit highly conserved ratios of net to gross primary productivity and leaf area to sapwood area.
• DESPOT provides insights into the emergent properties of tree growth driven by carbon allocation optimization.