Exploring plant diversity through soil DNA in Thai national parks for influencing land reform and agriculture planning

BACKGROUND: The severe deforestation, as indicated in national forest data, is a recurring problem in many areas of Northern Thailand, including Doi Suthep-Pui National Park. Agricultural expansion in these areas, is one of the major drivers of deforestation, having adverse consequences on local plant biodiversity. Conserving biodiversity is mainly dependent on the biological monitoring of species distribution and population sizes. However, the existing conventional approaches for monitoring biodiversity are rather limited. METHODS: Here, we explored soil DNA at four forest types in Doi Suthep-Pui National Park in Northern Thailand. Three soil samples, composed of different soil cores mixed together, per sampling location were collected. Soil biodiversity was investigated through eDNA metabarcoding analysis using primers targeting the P6 loop of the plastid DNA trnL (UAA) intron. RESULTS: The distribution of taxa for each sample was found to be similar between replicates. A strong congruence between the conventional morphology- and eDNA-based data of plant diversity in the studied areas was observed. All species recorded by conventional survey with DNA data deposited in the GenBank were detected through the eDNA analysis. Moreover, traces of crops, such as lettuce, maize, wheat and soybean, which were not expected and were not visually detected in the forest area, were identified. It is noteworthy that neighboring land and areas in the studied National Park were once used for crop cultivation, and even to date there is still agricultural land within a 5–10 km radius from the forest sites where the soil samples were collected. The presence of cultivated area near the forest may suggest that we are now facing agricultural intensification leading to deforestation. Land reform for agriculture usage necessitates coordinated planning in order to preserve the forest area. In that context, the eDNA-based data would be useful for influencing policies and management towards this goal.