Identification of plus trees for domestication: phenotypical description of Garcinia kola populations in Cameroon

Garcinia kola Heckel is a multipurpose medicinal tree with significant cultural and economic relevance in West and Central Africa. Although the domestication process is still in its early stages, most of the current research is laboratory-based, overlooking fields that can directly support the species’ development. We aimed to describe the morphological tree-to-tree variation of G. kola in Cameroon and identified plus trees representing the potential species’ ideotype. In total, 218 trees with 1,722 fruits and 4,553 seeds from 3 regions were analysed using 26 morphological descriptors. Statistical analysis was performed to select the top ten trees. The variation among study sites was identified using dendrogram and principal component analysis. Fruit seed mass was selected as the most important production-related criterion to identify the plus trees. It was a highly variable parameter (CV = 60%) with an average of 14.4 ± 8.56 g per fruit. The fruit/seed correlation revealed a strong link between high seed mass and large fruits. However, high fruit seed mass was not linked to any particular fruit and tree shape. Most of the plus trees originated in agroforestry systems. The overall phenotypic diversity within populations was greater than between them. In conclusion, G. kola’s domestication has not yet progressed enough to distinguish between wild and cultivated trees. Nonetheless, two ways of establishing future clonal cultivars were proposed based on fruit seed mass and fruit seed mass ratio. To improve the quality and uniformity of marketable seeds, selecting trees with above-average fruit seed mass was recommended.

Bootstrapping outperforms community-weighted approaches for estimating the shapes of phenotypic distributions

Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community-weighted approaches (e.g. abundance-weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community-weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted-averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill, allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community-weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community-weighting. Random sampling across all species outperformed both size- and abundance-biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Tree-to-tree variation in fruits of three populations of Trichoscypha acuminata (Engl.) in Cameroon

Within tree species, phenotypic variation is common and this can affect a species’ domestication. This study was therefore conducted to assess the phenotypic variation in Trichoscypha acuminata fruits in three populations (Nkenglikok, Ndengue and Nkoemvone) in the humid forest zone of Cameroon in view of understanding its selection potential for domestication. A total of 1080 fruits were collected for assessment from 45 trees. The measured traits were fruit diameter (FD), pulp thickness (PT), fruit mass (FM), seed mass (SM), pulp+shell mass (PM+Shell) and germination percentage. Data were analyzed using an ANOVA. Means were separated using Least Significant Difference (LSD) (p=5%). The results showed that significant (p < 0.05) variation was recorded in FM, PM+Shell, PT, FD, SM, and germination percentage between populations and trees. Nkenglikok seeds scored the best phenotypes including germination compared to the Nkoemvone and Ndengue seeds. The five trees with superior traits for selection (based on PM+Shell, the must usable part) were numbers TA/NK/6, TA/NK/14, TA/NK/12, TA/NK/10 and TA/NK/18. There were correlations between fruit traits on the one hand; and seed germination percentage and FD, SM, and PT on the other. We can conclude that there is phenotypic variation between trees of T. acuminata in terms of FM, FD, PM+Shell, SM, and germination percentage. The study's results can guide for future selection of targeted T. acuminata trees for domestication purposes.

Resilient Landscapes is powered by CIFOR-ICRAF. Our mission is to connect private and public actors in co-beneficial landscapes; provide evidence-based business cases for nature-based solutions and green economy investments; leverage and de-risk performance-driven investments with combined financial, social and environmental returns.

2024 All rights reserved    Privacy notice