Highlighting convergent evolution in morphological traits in response to climatic gradient in African tropical tree species: The case of genus Guibourtia Benn

Retrieving complete plastid genomes of endangered Guibourtia timber using hybridization capture for forensic identification and phylogenetic analysis

The high economic value and increased demand for timber have led to illegal logging and overexploitation, threatening wild populations. In this context, there is an urgent need to develop effective and accurate forensic tools for identifying endangered Guibourtia timber species to protect forest ecosystem resources and regulate their trade. In this study, a hybridization capture method was developed and applied to explore the feasibility of retrieving complete plastid genomes from Guibourtia sapwood and heartwood specimens stored in a xylarium (wood collection). We then carried out forensic identification and phylogenetic analyses of Guibourtia within the subfamily Detarioideae. This study is the first to successfully retrieve high-quality plastid genomes from xylarium specimens, with 76.95-99.97% coverage. The enrichment efficiency, sequence depth, and coverage of plastid genomes from sapwood were 16.73 times, 70.47 times and 1.14 times higher, respectively, than those from heartwood. Although the DNA capture efficiency of heartwood was lower than that of sapwood, the hybridization capture method used in this study is still suitable for heartwood DNA analysis. Based on the complete plastid genome, we identified six endangered or commonly traded Guibourtia woods at the species level. This technique also has the potential for geographical traceability, especially for Guibourtia demeusei and Guibourtia ehie. Meanwhile, Bayesian phylogenetic analysis suggested that these six Guibourtia species diverged from closely related species within the subfamily Detarioideae ca. 18 Ma during the Miocene. The DNA reference database established based on the xylarium specimens provides admissible evidence for diversity conservation and evolutionary analyses of endangered Guibourtia species.

Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny

Birdsong is a longstanding model system for studying evolution and biodiversity. Here, we collected and analyzed high quality song recordings from seven species in the family Estrildidae. We measured the acoustic features of syllables and then used dimensionality reduction and machine learning classifiers to identify features that accurately assigned syllables to species. Species differences were captured by the first 3 principal components, corresponding to basic frequency, power distribution, and spectrotemporal features. We then identified the measured features underlying classification accuracy. We found that fundamental frequency, mean frequency, spectral flatness, and syllable duration were the most informative features for species identification. Next, we tested whether specific acoustic features of species' songs predicted phylogenetic distance. We found significant phylogenetic signal in syllable frequency features, but not in power distribution or spectrotemporal features. Results suggest that frequency features are more constrained by species' genetics than are other features, and are the best signal features for identifying species from song recordings. The absence of phylogenetic signal in power distribution and spectrotemporal features suggests that these song features are labile, reflecting learning processes and individual recognition.