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Updated: Sep 11, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A unifying framework for understanding how edge effects reshape the structure, composition and function of forests.

Rebecca Banbury Morgan1, Tommaso Jucker1

  • 1School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK.

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Summary

Forest edges, common due to deforestation, alter forest dynamics. A new framework explains how edge age, climate, and structure influence these changes, reconciling varied outcomes across ecosystems.

Keywords:
aboveground biomassedge effectsforest fragmentationforest structurepatch contrastremote sensing

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Area of Science:

  • Ecology
  • Forestry
  • Environmental Science

Background:

  • Most global forests are near edges due to deforestation and fragmentation.
  • Forest edges exhibit altered microclimates (warmer, drier, brighter) and increased disturbance.
  • Edge effects on forest dynamics vary significantly across ecosystems, with poorly understood reasons.

Purpose of the Study:

  • To develop a unifying framework explaining variable forest edge effects.
  • To reconcile contrasting outcomes of forest edge creation.
  • To account for edge age, climatic context, and forest structure in edge effect studies.

Main Methods:

  • Identified four stages of forest edge development.
  • Proposed a framework integrating edge age, climate, and forest structure.
  • Utilized remote sensing data to analyze ecosystem change.

Main Results:

  • Demographic trajectories at forest edges are broadly similar across forest types after accounting for development stages.
  • Regional climate and forest structure explain variations in edge effects, such as biomass changes in tropical versus temperate forests.
  • The framework generates testable predictions for future research.

Conclusions:

  • A new framework reconciles diverse forest edge effects by considering edge age, climate, and structure.
  • Understanding these factors is crucial for predicting forest dynamics in fragmented landscapes.
  • Remote sensing offers powerful tools for testing ecological predictions at broad scales.