Altitudinal and slope gradients as key determinants of Robinia pseudoacacia growth

Robinia pseudoacacia is a major plantation species in northern China, yet quantitative assessments of its growth across topographic gradients remain limited. This study investigated the growth performance of R. pseudoacacia in relation to landform types (plains, hills, low mountains) and key topographic factors (altitude, slope) in Weifang, Shandong Province. Field surveys were conducted across 67 pure‑stand sample plots. One‑way ANOVA revealed that trees on plains had significantly greater mean diameter at breast height (DBH) and tree height than those on hills and low mountains. Linear regression showed significant negative correlations: DBH and height decreased by approximately 0.58 cm and 0.47 m per 100 m increase in altitude, and by 0.76 cm and 0.57 m per 10° increase in slope. Altitude explained slightly more variation (R² = 10.1-10.6%) than slope (R² = 5.5-6.8%). Allometric analysis indicated stable height-DBH scaling across landforms (ANCOVA, P = 0.776), with scaling exponents (b) of 0.59 (low mountain), 0.48 (hill), and 0.68 (plain). Our results quantitatively delineate growth patterns of R. pseudoacacia across topographic gradients, highlighting the strong association between gentle, low‑lying terrains and superior growth. These findings provide an empirical basis for site selection in afforestation projects, suggesting a preference for plain areas to enhance plantation productivity. Future studies should incorporate soil and microclimatic data to elucidate underlying mechanisms.

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Publication Details

Journal
PLoS ONE
Published
2026-08-26
DOI
https://doi.org/10.1371/journal.pone.0354736
Primary Topic
Forest ecology and management
Type
article
Field-Weighted Citation Impact
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article

Altitudinal and slope gradients as key determinants of Robinia pseudoacacia growth

Zhipeng Wang, Yuntao Li, Jiang Xin, Ning Wang et al.
PLoS ONE
Forest ecology and management
article

Altitudinal and slope gradients as key determinants of Robinia pseudoacacia growth

Zhipeng Wang, Yuntao Li, Jiang Xin, Ning Wang, Yanli Xiao, Liyan Hou, Zhikun Zhang, Yirui Shao, Jinfeng Nie
article en

Abstract

Robinia pseudoacacia is a major plantation species in northern China, yet quantitative assessments of its growth across topographic gradients remain limited. This study investigated the growth performance of R. pseudoacacia in relation to landform types (plains, hills, low mountains) and key topographic factors (altitude, slope) in Weifang, Shandong Province. Field surveys were conducted across 67 pure‑stand sample plots. One‑way ANOVA revealed that trees on plains had significantly greater mean diameter at breast height (DBH) and tree height than those on hills and low mountains. Linear regression showed significant negative correlations: DBH and height decreased by approximately 0.58 cm and 0.47 m per 100 m increase in altitude, and by 0.76 cm and 0.57 m per 10° increase in slope. Altitude explained slightly more variation (R² = 10.1-10.6%) than slope (R² = 5.5-6.8%). Allometric analysis indicated stable height-DBH scaling across landforms (ANCOVA, P = 0.776), with scaling exponents (b) of 0.59 (low mountain), 0.48 (hill), and 0.68 (plain). Our results quantitatively delineate growth patterns of R. pseudoacacia across topographic gradients, highlighting the strong association between gentle, low‑lying terrains and superior growth. These findings provide an empirical basis for site selection in afforestation projects, suggesting a preference for plain areas to enhance plantation productivity. Future studies should incorporate soil and microclimatic data to elucidate underlying mechanisms.

PLoS ONEVol. 21(8)
China Centre for Resources Satellite Data and Application (CN), Weifang University (CN)
Natural Science Foundation of Shandong Province
Zero hunger
Openalex Percentile: Top 7%
Forest ecology and management
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Altitudinal and slope gradients as key determinants of Robinia pseudoacacia growth — Zhipeng Wang, Yuntao Li, et al. · PLoS ONE (2026) | TGRS Research Map | TGRS