Background: Mangrove ecosystems have been the focus of global attention for their crucial role in sheltering coastal communities and retarding global climate change by sequestering ‘blue carbon’. China is relatively rich in mangrove diversity, with one-third of the ca. 70 true mangrove species and a number of mangrove associate species occurring naturally along the country’s coasts. Mangrove ecosystems, however, are widely threatened by intensifying human disturbances and rising sea levels. The urgent need to protect mangrove ecosystems could be assisted by using barcoding technology, which provides rapid species identification.

Results: To investigate this potential, 898 plant specimens were collected from 33 of the major mangrove sites in China. Based on the morphologic diagnosis, the specimens were assigned to 72 species, including all 28 true mangrove species and all 12 mangrove associate species recorded in China. Three chloroplast DNA markers rbcL, trnH-psbA, matK, and one nuclear marker ITS2 were chosen to investigate the utility of using barcoding to identify these species. According to the criteria of barcoding gaps in genetic distance, sequence similarity and phylogenetic monophyly, we propose that a single marker, ITS2, is sufficient to barcode the species of mangroves and their associates in China. Furthermore, rbcL or trnH-psbA can also be used to gather supplement confirming data. In using these barcodes, we revealed a very low level of genetic variation among geographic locations in the mangrove species, which is an alert to their vulnerability to climate and anthropogenic disturbances.

Conclusion: We suggest to use ITS2 to barcode mangrove species and terrestrial coastal plants in South China. The DNA barcode sequences we obtained would be valuable in monitoring biodiversity and the restoration of ecosystems, which are essential for mangrove conservation.

We collected 898 plant specimens from 33 mangrove sites along the coastlines of South China, from Hainan island to northernmost Zhejiang. Three plasmid barcoding regions rbcL, matK, trnHpsbA, and one nuclear barcoding gene ITS2 were amplified. Raw bidirectional sequences were assembled and manually checked by SeqMan (LASERGENE software package). Multiple sequences of each genomic region were aligned using MUSCLE in MEGA v7. We constructed neighbour-joining (NJ) trees, maximum likelihood (ML) trees for each candidate barcode, with supporting rates for nodes obtained from 1000 bootstrap replicates, and bayesian inference (BI) was performed to construct phylogeny trees for at most twelve orders. The NJ trees were generated in MEGA v7 based on the Kimura 2-parameter (K2P) genetic distance model. The ML trees were constructed using the RAxML program under the GTRCAT model. The BI trees were constructed by Markov chain Monte Carlo simulation under the GTR substitution model with gamma-distributed rate variation across sites and a proportion of invariable sites (“GTR + I + Γ”) in MrBayes v3.2.7a.