Investigating tree diversity and structure across varying land cover classes and altitudes in the savanna woodlands of Sudan

Climate and land use changes significantly endanger tree species’ structure and diversity in savanna woodlands. The destruction of these ecosystems hinders the achievement of several global environmental and development targets, notably SDGs 2, 13, and 15, which underscores the need for continuous tree monitoring to inform decision-making on biodiversity conservation. This study aims to characterize the relationships between tree diversity and structure across different land cover (LC) classes—Dense tree cover (D), Sparse tree cover (S), Grasslands (G), and Wetlands (W)—and altitudinal gradients (AGs) in Alain forest, located in Sudan’s savanna woodlands. A systematic sampling was used across varying AGs and LC types to collect data on tree species richness and structure in 926 circular plots. Tree diversity, measured using Shannon and Simpson indices, showed significant differences among LC classes (p < 0.05), with the highest diversity observed in D and the lowest in G. Both indices exhibited a significant negative correlation with AGs (Shannon: R = –0.33, p < 0.001; Simpson: R = –0.30, p < 0.001), indicating a decline in tree diversity with increasing elevation. Tree structural attributes also varied significantly across LC types: tree height and density were highest in D and lowest in G ( p < 0.05). While tree height showed a weak but significant negative correlation with altitude ( R = –0.106, p = 0.003), tree density did not ( R = –0.048, p = 0.185). Principal Component Analysis (PCA) revealed distinct clustering of LC classes based on combined diversity and structural attributes, with tree height and diversity indices contributing strongly to the first two principal components. These findings highlight the influence of LC and AGs on tree community structure and biodiversity in Alain forest, offering valuable insights for conservation and land-use planning. Further research based on more comprehensive datasets is recommended to boost scientific knowledge for biodiversity conservation and sustainable management.