Toward a deeper understanding of recovery trajectories in forest ecosystem restoration: a machine learning approach

Jenny Vivian; Robin Chazdon; Alison Shapcott; David Lee

doi:10.1111/rec.70483

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Toward a deeper understanding of recovery trajectories in forest ecosystem restoration: a machine learning approach

Journal article

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Toward a deeper understanding of recovery trajectories in forest ecosystem restoration: a machine learning approach

Jenny Vivian, Robin Chazdon, Alison Shapcott and David Lee

Restoration Ecology, Vol.Advanced access, e70483

02-Jul-2026

DOI: https://doi.org/10.1111/rec.70483

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Abstract

Forestry management and environment

Forestry not elsewhere classified

Acacia mangium

chronosequence

ecosystem hypervolume

emerging properties

forest restoration

machine learning

soil microbiome

Introduction The lack of integrated monitoring approaches and indicators hampers the understanding of forest restoration trajectories, as the recovery process involves multiple aspects of the ecosystem, which can respond differently to environmental changes due to their interactions and synergies. Objective We aimed to demonstrate that a highly integrated approach and adoption of machine learning methods can provide a deeper understanding of forest recovery trajectory, in a context where the subcomponents may vary at different rates. Specifically, we considered 184 ecosystem attributes, including geographical information, tree community characteristics, soil fertility, and soil microbial taxa and functions, to study the trajectory of forest restoration in former Acacia mangium plantations. These ecosystem characteristics encompass multiple dimensions of the ecosystem hypervolume, from which ecosystem properties emerge. Methods We analyzed and processed previously collected data on the spatial location, tree community, aboveground biomass, and soil microbial assemblages through unsupervised and supervised machine learning algorithms. Our approach is based on space-for-time substitution rather than surveys over time within sites. Results The analysis integrating the 184 ecosystem factors revealed that landcover types were similar only to the sequence nearest in age (e.g., 10-year-old plantation similar to the 2- and 24-year-old plantings), even when individual parameters recovered at different rates.

Details

Title: Toward a deeper understanding of recovery trajectories in forest ecosystem restoration: a machine learning approach
Authors: Jenny Vivian (Corresponding Author) - University of the Sunshine Coast
Robin Chazdon (Author) - University of the Sunshine Coast
Alison Shapcott (Author) - University of the Sunshine Coast
David Lee (Author) - University of the Sunshine Coast
Publication details: Restoration Ecology, Vol.Advanced access, e70483
Publisher: Wiley-Blackwell Publishing, Inc.
DOI: 10.1111/rec.70483
ISSN: 1526-100X
Copyright note: © 2026 The Author(s). Restoration Ecology published by Wiley Periodicals LLC on behalf of Society for Ecological Restoration. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Data Availability: The data that support the findings of this study are openly available in ML_Classification_for_forest_restoration at https://github.com/JennyVivian99/ML_Classification_for_forest_restoration.
Grants: Project Tarsier, 0980027198, Shell Pilipinas Corporation
Organisation Unit: Forest Industries Research Centre
Language: English
Record Identifier: 991243698602621
Output Type: Journal article

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