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Research Article

Structural diversity is a key driver of above-ground biomass in tropical forests

ORCID Icon, ORCID Icon & ORCID Icon
Pages 147-164 | Received 21 Nov 2022, Accepted 26 Oct 2023, Published online: 24 Nov 2023
 

ABSTRACT

Background

A gamut of abiotic and biotic factors is related to the amount of above-ground biomass (AGB) produced in ecosystems. Some factors have direct and others indirect relationships with AGB. Detailed analyses in tropical forests are few but much needed for better understanding the potential impacts of global change drivers and for mitigating impacts.

Aims

Here, we examined the relationship between AGB and different predictor variables and quantitatively evaluated their relative importance in lowland to lower montane deciduous and lower montane – montane evergreen forest types. We hypothesised that the relationship between AGB and climate, topography, structural diversity, species diversity (alpha and beta) and phylogenetic diversity would differ between the two forest types.

Methods

We inventoried trees from 114 plots (each 0.1 ha) and used partial least square structural equation modelling to test the direct and indirect relationship between AGB and the predictor variables.

Results

We found that structural diversity variables, stem density and tree girth, were significantly and positively related to AGB in both forest types, displaying a stronger relationship in montane evergreen forests (w = 0.65 for density and 0.89 for tree girth). In the deciduous forest, alpha and phylogenetic diversity were also important factors, whereas beta and phylogenetic diversity were important in the evergreen forest. The effects of topography and climate varied between forest types, with elevation and precipitation being related to AGB directly and indirectly through their relationship with structural diversity.

Conclusion

Our results suggest that structural diversity is a key driver of tropical forest biomass, both directly and indirectly. This fundamental understanding can aid in the predictive efforts of biodiversity conservation and forest management.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17550874.2023.2277282.

Acknowledgments

We thank the Karnataka Forest Department for providing the necessary permissions and assistance for the fieldwork. We also express sincere gratitude to all the anonymous reviewers and the Editor-in-Chief for their valuable comments, which contributed to improving the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Department of Biotechnology and Department of Space, Government of India (Grant no.-BT/Cood.II/10/02/2016), as a part of a project ‘Biodiversity characterization at community level in India using Earth observation data’.

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