Predicting current and future distribution of Hovenia dulcis Thunb. (Rhamnaceae) worldwide

Integrating remote sensing and UAV imagery for detection of invasive Hovenia dulcis Thumb. (Rhamnaceae) in urban Atlantic Forest remnants

The invasive species Hovenia dulcis is considered the main invasive species in the Atlantic Forest, capable of altering environmental conditions at a local scale and provoking profound changes in the composition of the plant community. Combining drone and satellite images can make forest monitoring more efficient, enabling a more targeted and effective response to contain the spread of invasive species. This research aimed to use high-resolution CBERS-4A satellite combined with drone images to detect invasive trees in forested areas of the Atlantic Forest. An object-oriented, supervised automatic classification was performed using the Dzetsaka Classification Tool and the Gaussian Mixture Model method. Additionally, georeferenced orthomosaics obtained by drones, totaling 150 ha, were used to confirm the identification of the invasive species. The entire forest area was surveyed to determine the tree community, where 72 random sample plots, each with a fixed area of 100 m2, were established. The calculated indices, such as the Shannon index (H') = 3.65 and uniformity (J') = 78%, demonstrate that the plant community has a high diversity of species. However, the invasive H. dulcis had the highest number of sampled individuals (146), being the species with the highest relative density (9.14) within the community and the second highest in relative frequency (5.10%), coverage importance value (8.85%), and importance value index (7.60%). The methodology employed to identify the invasive species through satellite, and drone images allowed for rapid and precise data collection and quantification of the invasive species, covering an area of 86.44 ha of the forest fragment, which corroborates the field data.

Climate Niche Modelling for Mapping Potential Distributions of Four Framework Tree Species: Implications for Planning Forest Restoration in Tropical and Subtropical Asia

Selecting tree species to plant for forest ecosystem restoration is critical but problematic. Knowing tree species’ climatic niches can help, but such information is limited for most tropical tree species. Consequently, the research presented here explored the use of climate and species record location data, to map the potential distribution of four tree species across tropical and subtropical Asia based on their modelled climatic niches. All were framework species, of proven effectiveness for forest restoration: Choerospondias axillaris (Roxb.) B.L. Burtt and A.W. Hill, Ficus hispida L.f., Hovenia dulcis Thunb., and Prunus cerasoides Buch.-Ham. ex D. Don. Potential species distributions were projected onto maps of known climatic conditions using the maximum entropy algorithm in Maxent software to predict where the climate is conducive for including each species in forest restoration trials. The models predicted species location very well for F. hispida and H. dulcis and fairly well for P. cerasoides and C. axillaris. Climatically suitable areas for C. axillaris were located mostly north of the equator, whilst those of F. hispida, H. dulcis and P. cerasoides extended south of the equator. The importance of each climatic niche variable differed among species. Driest-month precipitation was critical for F. hispida, as well as mean dry-season temperature for C. axillaris and P. cerasoides and cold-season precipitation for H. dulcis. In addition to aiding species selection for forest restoration, potential distribution maps based on climatic niche models can indicate where novel species for tree plantations might be successful and where species might become invasive. Applying such techniques to a large number of species will be needed to significantly improve species selection for forest ecosystem restoration.