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Functional traits and functional diversity metrics in aboveground biomass estimation in different physiognomies of Cerrado

Ruan Felipe Lima Caldeira (1-2)   , Eder Pereira Miguel (1), Juscelina Arcanjo Dos Santos (1), Mario Lima Dos Santos (3), Gileno Brito Azevedo (4), Eraldo Aparecido Trondoli Matricardi (1), Aldicir Scariot (2), Matheus Santos Martins (2)

iForest - Biogeosciences and Forestry, Volume 19, Issue 5, Pages 329-338 (2026)
doi: https://doi.org/10.3832/ifor4984-019
Published: Sep 01, 2026 - Copyright © 2026 SISEF

Research Articles


Growing concerns about climate change and biodiversity loss have increased the importance of research on vegetation ecology and ecosystem functioning for understanding forests and savannas. Functional diversity is a field of study that has expanded in recent years, aiming to explain ecosystem functions through organisms’ functional characteristics. The Cerrado, the second-largest biome in South America, is regarded as the world’s most biodiverse savanna and plays a crucial role in biomass accumulation. In this context, the present study aimed to investigate the role of functional traits in the accumulation of woody aboveground biomass (AGB) in the Cerrado by analyzing the relationship between Community Weighted Means (CWMs) of functional traits and functional diversity metrics across three distinct physiognomies (Cerrado Típico, Cerrado Denso, and Cerradão). We hypothesized that the relationships between biomass and functional diversity metrics would differ among physiognomies. To assess these relationships, we tested correlations between biomass and both CWMs and functional diversity metrics. We measured five traits for dominant species selected according to cumulative abundance criteria in each physiognomy: specific leaf area (SLA), wood density (WD), crown area (CA), maximum diameter (Dmax), and maximum height (Htmax). These traits were used to calculate the respective CWMs, along with five functional diversity metrics: Functional Richness (Fric), Functional Evenness (FEve), Functional Divergence (FDiv), Functional Dispersion (FDis), and Rao’s Quadratic Entropy (RaoQ). We used forest inventory data from the study areas to fit linear models for each physiognomy and mixed-effects models for the complete dataset, with physiognomic type as a random effect. The physiognomies differed in structural and taxonomic characteristics, displaying growth patterns proportional to vegetation stature. Functional differences were also observed: biomass in Cerradão was more strongly associated with functional traits, whereas biomass in Cerrado Denso and Cerrado Típico was more strongly associated with functional diversity metrics. The CWMs Dmax, WD, and CA, along with the diversity metrics Fric and FEve, were the predictors most frequently retained in the best-performing models. General models that included both CWMs and functional diversity metrics, or only CWM Dmax, outperformed models that included only Fric or excluded random effects.

  Keywords


Functional Diversity, Brazilian Savanna, Biomass Equation, Mixed Models

Authors’ address

(2)
Ruan Felipe Lima Caldeira 0009-0009-7691-198x
Aldicir Scariot 0000-0003-0771-3073
Matheus Santos Martins 0000-0003-1716-4354
Brazilian Agricultural Research Corporation (EMBRAPA), Park Estação Biológica, Brasília, DF, 70770-917 (Brazil)
(3)
Mario Lima Dos Santos 0000-0003-1679-9796
Brazilian Forest Service, SCEN, Trecho 2, Bl. H, 70818-900, Brasília, DF (Brazil)
(4)
Gileno Brito Azevedo 0000-0003-4811-9476
Federal University of Mato Grosso do Sul, UFMS, Campus Chapadão do Sul, 79560-000, Chapadão do Sul, MS (Brazil)

Corresponding author

 
Ruan Felipe Lima Caldeira
ruanflc1@gmail.com

Citation

Caldeira RFL, Miguel EP, Dos Santos JA, Dos Santos ML, Azevedo GB, Matricardi EAT, Scariot A, Martins MS (2026). Functional traits and functional diversity metrics in aboveground biomass estimation in different physiognomies of Cerrado. iForest 19: 329-338. - doi: 10.3832/ifor4984-019

Academic Editor

Angelo Rita

Paper history

Received: Sep 11, 2025
Accepted: Jun 23, 2026

First online: Sep 01, 2026
Publication Date: Oct 31, 2026
Publication Time: 2.33 months

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