Integrating species functional and architectural traits for improving crown width prediction in subtropical multispecies forests using nonlinear hierarchical models
Crown width (CW) is a critical predictor of individual tree development and forest ecosystem function. Effective CW models are well established for plantations and structurally simpler forests, yet their applicability to diverse, species-rich natural forests remains inadequate. In this study, we ana...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125002249 |
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| Summary: | Crown width (CW) is a critical predictor of individual tree development and forest ecosystem function. Effective CW models are well established for plantations and structurally simpler forests, yet their applicability to diverse, species-rich natural forests remains inadequate. In this study, we analyzed CW data from 1802 individual trees of 23 species in a subtropical natural forest to assess the effects of tree-specific variables, interspecies variability, neighborhood effects, and topographical factors on CW predictions. Specifically, we aimed to elucidate how variations in CW models among individual species can be accounted for based on species architectural and functional traits. Among various candidate base models, the logistic model most effectively captured the relationship between CW and diameter at breast height (DBH). Model accuracy was improved by incorporating individual tree height, crown length, neighborhood competition index, and elevation as explanatory variables. A nonlinear mixed-effects model highlighted the significant role of tree species identity as a random effect in accounting for interspecific variability in CW predictions. We integrated species-level functional and architectural traits as covariates in the hierarchical model. The results revealed that 62 % of the interspecific variation originally captured by the random effects of species was explained by leaf thickness and mean crown diameter to DBH ratio. Our results highlight the importance of accounting for species variability in CW modeling and suggest that including species traits as covariates significantly improves model accuracy and generalizability, particularly for previously neglected architectural traits. The results obtained in this study expand our understanding of crown growth patterns and offer an improved basis for modeling tree crown sizes in species-rich forests. |
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| ISSN: | 1574-9541 |