Scientists develop advanced methods to study forest ecosystem structure
Researchers from Nizhny Novgorod and China are developing advanced methods to study in detail the spatial structure and diversity of forest ecosystems, in order to forecast how ecosystems will respond to forest fires, human intervention or global climate change. The research is being conducted by scientists from the UNN Department of Ecology together with their colleagues from the Beijing University of Forestry and the Research Centre for Ecological and Environmental Sciences of the Chinese Academy of Sciences.
Two methods for analysing the spatial structure of forests have been combined: the random point process method based on data about real trees, which can generate new models and analyse vegetation patterns; and multifractal analysis, which helps to identify general patterns by examining the ecosystem at different scales.
"To apply the methodology, the coordinates of each tree are required. This time-consuming process has been tackled by Chinese colleagues, who have carried out detailed mapping of forest areas. In the future, it will be possible to determine the location and the species of trees from drones, using machine learning algorithms," said Vasily Yakimov, Head of the Ecology Department at the UNN Institute of Biology and Biomedicine.
It should be noted that Lobachevsky University ecologists have already carried out multifractal analysis of ecosystems in the Nizhny Novgorod region and other regions of Russia.
"We studied the structure of the forest community of the Oak Grove nature sanctuary at the University Botanical Garden (Nizhny Novgorod) and the meadow community in the vicinity of the Pustyn Reserve (Arzamas district of the Nizhny Novgorod region, UNN Biological Station). The data obtained make it possible to predict the response of ecosystems to global climate change and anthropogenic impact," Vasily Yakimov noted.
The research is part of the Priority 2030 Strategic Academic Leadership Programme and is supported by the National Natural Science Foundation of China and the Outstanding Young Talent Support Project of the Beijing University of Forestry. The results of the study have been published in the journal Forest Ecology and Management.