Files and links (1)
Preprint VersionCC BY V4.0, Open Access
Preprint
Forest ecosystem restoration potential unravelled through machine learning algorithms
ARPHA Preprints, Vol.2 December 2025
Pensoft Publishers
2025
Abstract
Decades of scientific development have led to a specialisation of environmental analyses, hampering the recognition of forest ecosystem restoration. However, an integrated approach is required to better understand the trajectory of forest recovery. In this study, we aimed to evaluate potential comprehensive approaches for forest restoration assessment by considering 184 environmental features, including geographical information, tree dimensions and biomass, soil abiotic parameters, microclimate, and soil microbial taxa and functions. All these characteristics constituted the dimensions of the ecosystem hypervolume and its emerging properties, related to naturally regenerating Acacia mangium plantations. Through unsupervised and supervised machine learning algorithms, we recognised the known time lag between the modification of ecosystem properties and the macroscopic aspects, such as the aboveground biomass. While the sectorial analysis highlighted the recovery of numerous characteristics by the 10th year after plantation establishment (e.g., tree biomass), the analysis integrating numerous ecosystem parameters highlighted the sequential similarity along the chronosequence, with only the 24-year-old plantation approaching recovery as measured in an adjacent remnant forest. The presence of keystone parameters (environmental variables with disproportionate effect on the ecosystem restoration relative to their value or variance) and the lagged response of ecosystem properties to drivers of environmental changes call for a more comprehensive approach to assess the achievement of restoration goals. Together with recent efforts to include machine learning analyses to answer ecological questions, our study brings unprecedented evidence on how leveraging advances in this analytical field can prompt a better understanding and management of recovering forests, considering multiple intertwined environmental factors.
Details
- Title
- Forest ecosystem restoration potential unravelled through machine learning algorithms
- Authors
- Jenny Vivian (Corresponding Author) - University of the Sunshine Coast, Queensland, Forest Research InstituteRobin Chazdon - University of the Sunshine Coast, Queensland, Forest Research InstituteAlison Shapcott - University of the Sunshine Coast, Queensland, Centre for BioinnovationDavid Lee - University of the Sunshine Coast, Queensland, Forest Research Institute
- Publication details
- ARPHA Preprints, Vol.2 December 2025
- Publisher
- Pensoft Publishers
- Date published
- 2025
- DOI
- 10.3897/arphapreprints.e181100
- Copyright note
- This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- Organisation Unit
- Tropical Forests and People Research Centre; Forest Industries Research Centre; School of Science, Technology and Engineering; Forest Research Institute; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 991192343802621
- Output Type
- Preprint
Metrics
1 Record Views