The study of forest fragmentation has gained increased importance in light of the ongoing human-induced deforestation in tropical regions. Forest fragmentation can alter the structure and functioning of tropical forest remnants far from the forest edge. However, there is limited understanding of the long-term and large-scale consequences of fragmentation on the structural characteristics of tropical forests. Dynamic forest models may improve our understanding by simulating long-term ecological processes induced by forest fragmentation under different biogeographical settings. We evaluated the ability of a state-of-the-art forest growth model to reproduce changes in the three-dimensional distribution of plants in Amazonian forests affected by fragmentation. Forest structural metrics extracted from the model simulations were compared with those obtained using high-resolution terrestrial laser scans (TLS) of a 40-year-old forest fragmentation experiment in the Brazilian Amazon. Our chosen model had some functionality to study edge effects, but did not, in its existing form, capture their complexity, or reproduce the vertical distribution of plant material in old-growth forests. However, by adding a parameter to optimize tree mortality rates at varying edge distances, and incorporating a model of understory vegetation, the model performance improved substantially. Total Plant Area Index (PAI), and PAI at varying height intervals, amongst other metrics, showed greatly improved responses to edge effects, closer to the real-world observations by TLS. Our study highlights the potential of well-calibrated forest models in examining the mechanisms and impact of environmental changes on tropical forests.
DOWNIE Eleanor;
FISCHER Rico;
KNAPP Nikolai;
CAMARGO Jose Luis;
ALIBAKHSHI Sara;
NUNES Matheus Henrique;
GHIZONI SANTOS Erone;
MAEDA Eduardo;
2026-06-16
ELSEVIER
JRC146600
1878-0512 (online),
https://doi.org/10.1016/j.ecoinf.2026.103825,
https://publications.jrc.ec.europa.eu/repository/handle/JRC146600,
10.1016/j.ecoinf.2026.103825 (online),
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