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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Demography, dynamics and data: building confidence for simulating changes in the world's forests.

Annemarie H Eckes-Shephard1, Arthur P K Argles2, Bogdan Brzeziecki3

  • 1Department of Physical Geography and Ecosystem Science, Lund University, Lund, 22362, Sweden.

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|October 23, 2025
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Summary

Vegetation demographic models (VDMs) show varied carbon recovery rates after disturbances. This study benchmarks nine VDMs, identifying critical calibration needs for accurate forest projections.

Keywords:
demographic vegetation model benchmarkingforest demographygrowth–mortality dynamicsland‐surface modellingmodel intercomparisonpostdisturbance recoveryself‐thinningvegetation carbon

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Area of Science:

  • Ecology
  • Forestry
  • Climate Science

Background:

  • Vegetation demographic models (VDMs) are crucial for simulating forest dynamics under climate and land-use change.
  • VDMs are increasingly used in Earth System Models but lack standardized performance comparisons.
  • Understanding forest responses requires accurate VDM simulations of carbon cycling and biomass.

Purpose of the Study:

  • To quantitatively benchmark the performance of nine Vegetation Demographic Models (VDMs).
  • To assess VDM accuracy in predicting forest growth, carbon turnover, biomass, and size distributions.
  • To identify key model parameters for improving forest response predictions.

Main Methods:

  • Benchmarking nine VDMs against observational data from boreal, temperate, and tropical forests.
  • Simulating models under consistent climate conditions for at least 420 years post-disturbance.
  • Analyzing post-disturbance recovery trajectories, initial regrowth rates, and mature forest carbon content.

Main Results:

  • VDMs exhibited significant variability in post-disturbance carbon recovery trajectories within observational ranges.
  • Initial regrowth rates varied substantially across biomes and models, influenced by initial forest states.
  • Models accurately captured mature forest carbon but showed compensating errors in growth and mortality rates.

Conclusions:

  • Growth and mortality rates are critical calibration targets for improving VDMs.
  • Refining post-disturbance establishment conditions is essential for VDM development.
  • Specific benchmarking variables are needed to enhance predictions of forest responses to environmental change.