As one of the most cold and salt-tolerant mangrove species, Kandelia obovata is widely distributed in China. Here, we report the complete chloroplast genome sequence K. obovata (Rhizophoraceae) obtained via next-generation sequencing, compare the general features of the sampled plastomes of this species to those of other sequenced mangrove species, and perform a phylogenetic analysis based on the protein-coding genes of these plastomes. The complete chloroplast genome of K. obovata is 160,325 bp in size and has a 35.22% GC content. The genome has a typical circular quadripartite structure, with a pair of inverted repeat (IR) regions 26,670 bp in length separating a large single-copy (LSC) region (91,156 bp) and a small single-cope (SSC) region (15,829 bp). The chloroplast genome of K. obovata contains 128 unique genes, including 80 protein-coding genes, 38 tRNA genes, 8 rRNA genes and 2 pseudogenes (ycf1 in the IRA region and rpl22 in the IRB region). In addition, a simple sequence repeat (SSR) analysis found 108 SSR loci in the chloroplast genome of K. obovata, most of which are A/T rich. IR expansion and contraction regions were compared between K. obovata and five related species: two from Malpighiales and three mangrove species from different orders. The mVISTA results indicated that the genome structure, gene order and gene content are highly conserved among the analyzed species. The phylogenetic analysis using 54 common protein-coding genes from the chloroplast genome showed that the plant most closely related to K. obovata is Ceriops tagal of Rhizophoraceae. The results of this study provide useful molecular information about the evolution and molecular biology of these mangrove trees.

Hopea hainanensis is a large evergreen tree belonging to the family Dipterocarpaceae. It is currently listed as an endangered species on the International Union for Conservation of Nature’s (IUCN) Red List of Threatened Species. It is found primarily on China’s Hainan Island and northern Vietnam. To protect the plant species with extremely small populations Hopea hainanensis and understand its community stability and the inter-specific associations between Hopea hainanensis and other occurring species, we set up 16 quadrats in the areas where wild Hopea hainanensis populations occurred on Hainan Island to investigate the composition of plant species in the quadrats. We used the M. Godron Community stability index to quantify community stability. Based on the two × two contingency tables, the statistic X², association coefficient (AC), and Pearson correlation coefficient (PCC), percentage co-occurrence (PC), and OI index (OI) analyze the degree of association between Hopea hainanensis and the dominant species in each layer. The results showed that the relationship between Hopea hainanensis and the dominant species in the community was not significant. In addition, there were both positive and negative associations between Hopea hainanensis and other examined six tree species, positive associations with 13 shrub species, positive associations with two herbs, and negative associations with species of 7 species. The connection between Hopea hainanensis and herbs is more complex than that between trees, shrubs, and the dominant species. M. Godron’s stability analysis showed that the community where Hopea hainanensis occurred is relatively unstable (36.84/63.16). The ecology is somewhat fragile, indicating that the community is still at the early or middle stage of competing with other species for resource utilization. The stability of community structure and function is a suitable environment for Hopea hainanensis and finally achieve the purpose of adequate protection, we should choose species with strong positive associations for protecting and recovering Hopea hainanensis.

Habitat fragmentation has led to a reduction in the geographic distribution of species, making small populations vulnerable to extinction due to environmental, demographic, and genetic factors. The wild plant Chieniodendron hainanense, a species with extremely small populations, is currently facing endangerment and thus requires urgent conservation efforts. Understanding its genetic diversity is essential for uncovering the underlying mechanisms of its vulnerability and for developing effective conservation strategies. In our study, we analyzed 35 specimens from six different populations of C. hainanense using genotyping-by-sequencing (GBS) and single nucleotide polymorphism (SNP) methodologies. Our findings indicate that C. hainanense has limited genetic diversity. The observed heterozygosity across the populations ranged from 10.79 to 14.55%, with an average of 13.15%. We categorized the six populations of C. hainanense into two distinct groups: (1) Diaoluoshan and Baishaling, and (2) Wuzhishan, Huishan, Bawangling, and Jianfengling. The genetic differentiation among these populations was found to be relatively weak. The observed loss of diversity is likely a result of the effects of natural selection.

Mangrove ecosystems play a crucial role in coastal environments. However, due to the complexity of mangrove distribution and the similarity among different categories in remote sensing images, traditional image segmentation methods struggle to accurately identify mangrove regions. Deep learning techniques, particularly those based on CNNs and Transformers, have demonstrated significant progress in remote sensing image analysis. This study proposes TCCFNet (Two-Channel Cross-Fusion Network) to enhance the accuracy and robustness of mangrove remote sensing image semantic segmentation. This study introduces a dual-backbone network architecture that combines ResNet for fine-grained local feature extraction and Swin Transformer for global context modeling. ResNet improves the identification of small targets, while Swin Transformer enhances the segmentation of large-scale features. Additionally, a Cross Integration Module (CIM) is incorporated to strengthen multi-scale feature fusion and enhance adaptability to complex scenarios. The dataset consists of 230 high-resolution remote sensing images, with 80% used for training and 20% for validation. The experimental setup employs the Adam optimizer with an initial learning rate of 0.0001 and a total of 450 training iterations, using cross-entropy loss for optimization. Experimental results demonstrate that TCCFNet outperforms existing methods in mangrove remote sensing image segmentation. Compared with state-of-the-art models such as MSFANet and DC-Swin, TCCFNet achieves superior performance with a Mean Intersection over Union (MIoU) of 88.34%, Pixel Accuracy (PA) of 97.35%, and F1-score of 93.55%. Particularly, the segmentation accuracy for mangrove categories reaches 99.04%. Furthermore, TCCFNet excels in distinguishing similar categories, handling complex backgrounds, and improving boundary detection. TCCFNet demonstrates outstanding performance in mangrove remote sensing image segmentation, primarily due to its dual-backbone design and CIM module. However, the model still has limitations in computational efficiency and small-target recognition. Future research could focus on developing lightweight Transformer architectures, optimizing data augmentation strategies, and expanding the dataset to diverse remote sensing scenarios to further enhance generalization capabilities. This study presents a novel mangrove remote sensing image segmentation approach—TCCFNet. By integrating ResNet and Swin Transformer with the Cross Integration Module (CIM), the model significantly improves segmentation accuracy, particularly in distinguishing complex categories and large-scale targets. TCCFNet serves as a valuable tool for mangrove remote sensing monitoring, providing more precise data support for ecological conservation efforts.

Dendropanax oligodontus, which belongs to the family Araliaceae, is an endemic and endangered species of Hainan Island, China. It has potential economic and medicinal value owing to the presence of phenylpropanoids, flavonoids, triterpenoids, etc. The analysis of the structure and characteristics of the D. oligodontus chloroplast genome (cpDNA) is crucial for understanding the genetic and phylogenetic evolution of this species. In this study, the cpDNA of D. oligodontus was sequenced for the first time using next-generation sequencing methods, assembled, and annotated. We observed a circular quadripartite structure comprising a large single-copy region (86,440 bp), a small single-copy region (18,075 bp), and a pair of inverted repeat regions (25,944 bp). The total length of the cpDNA was 156,403 bp, and the GC% was 37.99%. We found that the D. oligodontus chloroplast genome comprised 131 genes, with 86 protein-coding genes, 8 rRNA genes, and 37 tRNAs. Furthermore, we identified 26,514 codons, 13 repetitive sequences, and 43 simple sequence repeat sites in the D. oligodontus cpDNA. The most common amino acid encoded was leucine, with a strong A/T preference at the third position of the codon. The prediction of RNA editing sites in the protein-coding genes indicated that RNA editing was observed in 19 genes with a total of 54 editing sites, all of which involved C-to-T transitions. Finally, the cpDNA of 11 species of the family Araliaceae were selected for comparative analysis. The sequences of the untranslated regions and coding regions among 11 species were highly conserved, and minor differences were observed in the length of the inverted repeat regions; therefore, the cpDNAs were relatively stable and consistent among these 11 species. The variable hotspots in the genome included clpP, ycf1, rnK-rps16, rps16-trnQ, atpH-atpI, trnE-trnT, psbM-trnD, ycf3-trnS, and rpl32-trnL, providing valuable molecular markers for species authentication and regions for inferring phylogenetic relationships among them, as well as for evolutionary studies. Evolutionary selection pressure analysis indicated that the atpF gene was strongly subjected to positive environmental selection. Phylogenetic analysis indicated that D. oligodontus and Dendropanax dentiger were the most closely related species within the genus, and D. oligodontus was closely related to the genera Kalopanax and Metapanax in the Araliaceae family. Overall, the cp genomes reported in this study will provide resources for studying the genetic diversity and conservation of the endangered plant D. oligodontus, as well as resolving phylogenetic relationships within the family.

Forest biomass, as a carrier of carbon, is an important indicator for judging forest productivity, stability and sustainable development capacity. Using the survey data of sample plots in eight forest areas in central Hainan, the biomass distribution of tropical mountain rainforests in National Park of Hainan Tropical Rainforest in different community sizes, diameter classes, altitudes and spaces was measured to explore the relationship between forest biomass and environmental factors. The results show that (1) the total area of tropical montane rainforests in National Park of Hainan Tropical Rainforest was about 983.70 km², distributed within an altitude range of 700–1300 m; the total aboveground biomass was about 25.208 million tons, which decreased first and then increased with increasing altitude, with an average aboveground biomass per unit area of 236.00 t·hm⁻²; (2) the primary forest accounted for 83.23% of the total aboveground biomass of the tropical mountain rainforest with only 29.84% of the total area, and the aboveground biomass per unit area was generally higher than that of the secondary forest; and (3) medium- and large-diameter trees were the main carriers of aboveground biomass in tropical mountain rain forests. More than 83.73% of the aboveground biomass was concentrated in large-diameter trees. The results of this study provide a reference for others aiming to perform measurement and evaluation of the carbon sink and the capacity for carbon neutrality in tropical rainforest ecosystems or to maintain regional biodiversity.