Abstract
Background: Biotic and abiotic disturbances such as frequent wildfires and herbivory contribute to maintain trees and grasses coexistence in savanna ecosystems. In comparison to stems and leaves, exposed to fire and herbivory, the roots, protected by being belowground, are less affected by these disturbances. Therefore, indirect estimation of belowground biomass (BGB) of savanna trees from simple allometric relations based on stem measurements can lead to major biases.
 Aims: In this study we explored how the Leaf ontogenetic change index (LOCI), a quantitative index based on leaf heteroblastic development, can provide an accurate estimate of BGB in Cussonia arborea, a widespread species in West African humid savannas.
 Methodology: We examined leaf morphometrics on post-fire resprouts of 40 individuals to assess whether LOCI can inform on plant age. We then analyzed by log-level regressions the variation of LOCI in relation to plant stem volume. Subsequently, we studied the variation of BGB according to stem volume, and as a function of both stem volume and LOCI, which allowed us to evaluate the contribution of LOCI to BGB estimation. BGB was obtained destructively by digging up roots and weighing total dry mass of 25 individuals including small and large trees. Statistical analyses were done with the R software.
 Place and Duration of Study: Study was performed in the Lamto Scientific Reserve, Côte d’Ivoire, between May 2020 and June 2021.
 Results: Using the stem volume as single explanatory variable of BGB, the regression model provided an adjusted R2 of 0.71. Association of the stem volume with LOCI increased the adjusted R2 from 0.71 to 0.90.
 Conclusion: Combining LOCI with a measure of stem size provides better estimate of BGB in C. arborea compared to estimate based on stem size only. Since a large proportion of woody species in frequently disturbed environments exhibit an overall strategies promoting persistence, future works should evaluate how these strategies are modulated during ontogeny and can explain biomass variation over time.
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