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Updated: Jun 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Predicting and understanding forest dynamics using a simple tractable model.

Drew W Purves1, Jeremy W Lichstein, Nikolay Strigul

  • 1Computational Ecology and Environmental Science Group, Microsoft Research, Cambridge CB3 0FP, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|October 31, 2008
PubMed
Summary
This summary is machine-generated.

The perfect-plasticity approximation (PPA) accurately predicts forest dynamics and species succession across diverse soils. This model, using tree allometry, growth, and mortality, forecasts future forest composition driven by species

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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Published on: October 16, 2018

Related Experiment Videos

Last Updated: Jun 28, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Area of Science:

  • Forest Ecology
  • Quantitative Ecology
  • Plant Ecology

Background:

  • Forest dynamics are complex, influenced by individual tree traits and environmental factors.
  • Predictive models are crucial for understanding and managing forest ecosystems.
  • The perfect-plasticity approximation (PPA) offers an analytically tractable approach to modeling forest dynamics.

Purpose of the Study:

  • To estimate PPA parameters for common tree species across different soil types in the US Lake States.
  • To validate PPA predictions of forest stand development and ecological succession over 100 years.
  • To identify key drivers of species succession using PPA-derived performance metrics.

Main Methods:

  • Estimated tree allometry, growth, and mortality parameters from short-term inventory data.
  • Implemented 100-year PPA simulations for multiple species on four soil types.
  • Compared PPA predictions with empirical chronosequences of stand development.

Main Results:

  • PPA accurately predicted basal area dynamics, ecological succession timing and magnitude, and diameter distributions.
  • Early- (H(20)) and late-successional (Z*) performance metrics correctly identified species' successional roles.
  • Species succession is driven by both understory and canopy performance, with mortality playing a larger role than growth.

Conclusions:

  • The PPA is a reliable tool for forecasting forest dynamics and species succession.
  • Mortality differences are key drivers of shifts in forest dominance.
  • PPA predictions align with observed forest changes, including regeneration failures and shifts to new dominants on certain soils.