Height–diameter allometry in African monodominant forest close to mixed forest

Panzou, Grâce Jopaul Loubota; Bocko, Yannick E.; Mavoungou, Alain Yves; Loumeto, Jean-Joël

doi:10.1017/s0266467421000183

Cited by 5 publications

(5 citation statements)

References 42 publications

(1 reference statement)

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“…Asymptotic models have been demonstrated to better describe height–diameter allometry for aboveground estimation in tropical forests. The Michaelis–Menten model was found to provide a good fit for height–diameter data in central Africa (Fayolle et al, 2016; Loubota Panzou et al, 2021). In each forest site, the Michaelis–Menten model has been used to fit height–diameter allometric relationships for liana‐loaded trees and liana‐free trees using the following equation based on height ( H ) and diameter ( D ) of a tree i belonging to liana factor (liana‐loaded trees and liana‐free trees) l in each forest site:

{H}_{il}={a}_l\times {D}_{il}/\left({b}_l+{D}_{il}\right),

where a and b are model parameters.…”

Section: Methodsmentioning

confidence: 99%

Intensity, determinants, and impacts of liana load on tropical trees in central Africa

et al. 2022

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Lianas can create dense foliage that reduces the light available for the host trees on which they grow and can directly damage the host trees during their development. A better estimation of liana load and understanding of the determinants and impacts of liana load in tropical forests are both important in community ecology and ecosystem science. This has, however, never been studied to date in central Africa. Here, we evaluated the intensity, determinants, and impacts of liana load in three forest sites in central Africa. We determined the liana load categories for all trees belonging to 78 of the most dominant tree species (2683 trees in total), with tree diameters ranging from 10 to 250 cm. The liana load was visually estimated as the liana cover on host tree using simple scale indices, and the proportion of trees covered by lianas defined as the liana prevalence was estimated for each species. Overall, 42% of the 2683 trees were liana‐loaded and the number of liana‐loaded trees was higher for smaller diameter classes in the three forest sites. Within each forest site, there was a significant difference in liana prevalence among coexisting species. Taller trees with shallow crowns were less covered by liana at the tree level, whereas trees belonging to light‐demanding and wind‐dispersed species showed a lower liana prevalence rate at the species level. In each forest site, the liana load significantly affected tree height–diameter allometry, with liana‐loaded trees being shorter for the same diameter. Lianas promote the structural complexity and influence tree diversity and ecosystem functioning of tropical forests.

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{H}_{il}={a}_l\times {D}_{il}/\left({b}_l+{D}_{il}\right),

where a and b are model parameters.…”

Section: Methodsmentioning

confidence: 99%

Intensity, determinants, and impacts of liana load on tropical trees in central Africa

et al. 2022

Self Cite

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“…The Michaelis-Menten model (M6) was first developed to model enzyme kinetics in chemistry [82]; however, it has been widely employed for modeling height-diameter relationships of various tree species (e.g., [31,[83][84][85]). This model has two biologically meaningful parameters that represent the maximum asymptotic height (β 0 ) a tree can attain, and the steepness (shape) of the curve (β 1 ) that describes the rate of increase in height [84].…”

Section: Base Model Selectionmentioning

confidence: 99%

“…They found out that plants exhibit a striking similarity in allometry across taxonomic lineages, climate zones, biomes, and disturbance regimes [14]. Until now, few attempts have been made to develop robust height prediction models for Afromontane tree species, while numerous models exist for different tree species in temperate (e.g., [15,[23][24][25][26]) as well as tropical forests [10,[27][28][29][30][31]. In Ethiopia, the existing models are limited in scope and geographic applicability [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia

Teshome,

Braz,

Torres

et al. 2024

Forests

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Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.

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“…In addition, tropical forests contain 40-50% of the carbon stored in terrestrial vegetation (Pan et al 2011;Feldpausch et al 2012), with accurate quantification of these stocks underpinning policies to mitigate CO 2 emissions such as IPCC recommendations and the UN-REDD+ program (Gibbs et al 2007). However, there are still large uncertainties associated with tropical forest carbon stock estimations (Panzou et al 2021). This is partly because the variation in biomass amongst different types of tropical forest is poorly quantified, particularly in Africa.…”

Section: Introductionmentioning

confidence: 99%

Floristic and structural distinctness of monodominant Gilbertiodendron dewevrei forest in the western Congo Basin

Heimpel,

Ahrends,

Dexter

et al. 2024

plecevo

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Background and aims – The forests of the Congo Basin contain high levels of biodiversity, and are globally important for carbon storage. In order to design effective conservation strategies, and to accurately model carbon stocks, a fine-scale understanding of the different forest types that make up this forest block is needed. Monodominant Gilbertiodendron dewevrei forest covers large areas of the Congo Basin, but it is currently unclear whether it is sufficiently distinct from adjacent mixed terre firme forest to warrant separate treatment for conservation planning and carbon calculations. This study aimed to compare the structure and diversity of monodominant and mixed forest, and ask whether there is a unique vascular plant community associated with G. dewevrei forest. Material and methods – We utilised a combination of plot data and herbarium specimens collected in the Sangha Trinational (a network of protect areas in Cameroon, Central African Republic, and the Republic of Congo). Plot inventories were used to compare G. dewevrei forest and mixed forest for stem density, basal area, above ground biomass, stem size distribution, species diversity, and species composition. In addition, a database of 3,557 herbarium specimens was used to identify species of vascular plant that are associated with G. dewevrei forest. Key results – Gilbertiodendron dewevrei forest is distinct in both structure and species composition from mixed forest. Gilbertiodendron dewevrei forest has a lower stem number (of trees ≥ 10 cm), but a greater proportion of larger trees (> 70 cm), suggesting higher carbon stocks. The species composition is distinct from mixed forest, with 56 species of vascular plant significantly associated with G. dewevrei forest. Conclusion – Monodominant G. dewevrei forest in the Sangha Trinational is both compositionally and structurally distinct from mixed forest. We therefore recommend this forest type be considered separately from mixed forest for conservation planning and carbon stock calculations.

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Height–diameter allometry in African monodominant forest close to mixed forest

Cited by 5 publications

References 42 publications

Intensity, determinants, and impacts of liana load on tropical trees in central Africa

Intensity, determinants, and impacts of liana load on tropical trees in central Africa

Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia

Floristic and structural distinctness of monodominant Gilbertiodendron dewevrei forest in the western Congo Basin

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