Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach

• Published in: Environmental monitoring and assessment 2024-05, Vol.196 (5), p.434-434, Article 434
• Main Authors: Jaiswal, Neha, Jayakumar, S.
• Format: Article
• Language: English
• Subjects: aboveground biomass; Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Biomass; Carbon; Carbon cycle; carbon sinks; Climate change; conservation areas; Earth and Environmental Science; Ecology; Ecotoxicology; Elevation; Environment; Environmental Management; Estimation; Forest biomass; Forest ecosystems; forests; Generalized linear models; global carbon budget; Learning algorithms; Machine learning; Monitoring/Environmental Analysis; Precipitation; regression analysis; Root-mean-square errors; Sanctuaries; soil; Species richness; Stand structure; Statistical models; temperature; Terrestrial ecosystems; tree and stand measurements; Trees; Wildlife; Wildlife refuges; Wildlife sanctuaries
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-024-12591-5

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal–Wallis tests did not reveal a significant relationship ( p  = 0.09) between TCS and elevation, linear regression showed a weak correlation ( R 2  = 0.002, p