Bridging implementation gaps to connect large ecological datasets and complex models.

Area of Science:

• Ecology
• Forestry
• Computational Biology

Background:

• Merging statistical methods with simulation models is key for ecological inference and forecasting.
• Challenges include data-model matching, initial conditions, and high dimensionality.

Purpose of the Study:

• To illustrate complexities and solutions in integrating ecological field data with mechanistic models.
• To analyze tree-ring data to constrain forest simulation model trajectories.
• To infer long-term competitive dynamics between Picea mariana and Picea glauca.

Main Methods:

• Utilized tree-ring basal area reconstructions from Denali National Park.
• Constrained successional trajectories using the University of Virginia Forest Model Enhanced (UVAFME).
• Estimated bias correction for stand age and improved parameter estimates.

Main Results:

• Incorporating tree-ring data improved parameter estimates and bias correction for stand age.
• Higher parameter values for Picea mariana's minimum growth under stress and Picea glauca's maximum growth rate improved coexistence simulations.
• Simulations suggest Picea glauca may outcompete Picea mariana under climate change.

Conclusions:

• Integrating tree-ring data with forest gap models enhances ecological inference and forecasting.
• Implementation challenges are critical for advancing data-model integration in ecology.
• Findings support Picea glauca's competitive advantage under future climate scenarios.