Morphological Response of Urban Trees to Pruning: A Case Study of Acacia auriculiformis Across Size Classes
Year: 2025
Type: article
Abstract: Pruning is a regular and essential urban tree maintenance practice aimed at sustaining overall health, ecosystem services, and public safety. However, knowledge of post-pruning recovery dynamics remains limited, which in turn hinders accurate assessments of growth and ecological functions. To address this, we examined recovery dynamics of Acacia auriculiformis, a common urban species. Tree height and crown radius were recorded monthly for 12 months after pruning. Trees were classified into two size groups based on diameter at breast height (DBH, trunk diameter measured at 1.3 m above ground): medium (DBH < 45 cm) and large (DBH ≥ 45 cm). A generalized linear mixed model (GLMM), appropriate for repeated measures and non-normal data, was fitted using a Tweedie distribution and a log-link function to model the recovery pattern. Results showed continuous growth over time, with medium-sized trees presenting significantly higher crown radius growth than large trees (p = 0.006), while height growth did not differ (p = 0.788). The best model for height included time (AIC = −846.4), whereas crown recovery was best modelled by time and size class (AIC = −1586.6). These findings demonstrate that, in this study, medium-sized A. auriculiformis generally recover faster, especially in crown expansion. This exploratory study suggests that tree size may influence post-pruning recovery and can provide a reference for subsequent differentiated management studies. The morphological modeling further provides preliminary quantitative evidence for annual recovery dynamics in urban A. auriculiformis.
Source: Forests
DOI: 10.3390/f16121826
Authors: Kaiheng Liu, Nancai Pei, Yanjun Sun, Jiameng Zhou, Wei Guo, Can Lai
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G292072102
OpenAlex ID: W4417047716
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