The species distribution model based on the random forest algorithm reveals the distribution patterns of Neophocaena asiaeorientalis

• Published in: Scientific reports Vol. 15; no. 1; pp. 10037 - 10
• Main Authors: Rongcheng, Rao, Yi, Huang, Jialing, Min, Ying, Yang, Fanning, Liu, Xiya, Wu, Xinyuan, Shi, Caigang, Li, Yingen, Dai, Qinghai, Huang, Jinxiang, Yu, Jianglong, Que
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/601; 704/158/1144; 704/158/2459; 704/158/670; 704/158/672; Algorithms; Animals; China; Conservation biology; Conservation of Natural Resources; Distribution patterns; Ecosystem; Endangered aquatic species; Endangered Species; Environmental DNA; Environmental factors; Environmental quality; Geographical distribution; Habitat preference; Habitat preferences; Habitats; Humanities and Social Sciences; Hydrographic environment; Hydrology; Machine learning; Machine learning algorithm; multidisciplinary; Plankton; Population studies; Porpoises - physiology; Prey; Random Forest; Science; Science (multidisciplinary); Seasonal variations; Surface water; China; Conservation biology; Endangered aquatic species; Habitat preference; Hydrographic environment; Machine learning algorithm
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-92508-5

The Species Distribution Model (SDM) provides a crucial foundation for the conservation of the Yangtze finless porpoise (YFP), a critically endangered freshwater cetacean endemic to China. In this study, we conducted population and habitat surveys, and employed the Random Forest algorithm (RF) to construct SDMs. We found that the habitat preference of YFP shows complex seasonality. Cyanobacteria and total phosphates have been identified as the predominant factors influencing the YFP distributions by affecting prey resources. We emphasize that ascertaining the presence and pseudo-absence points of YFP, in conjunction with the selection of key factors, constitutes the foundational element in the construction of SDMs. We suggest that the incorporation of techniques such as environmental DNA could expand the range of environmental factors, particularly with regard to the distribution of prey resources at the genus or species level. This study provides guidance for the SDMs of YFP and demonstrates the potential of machine learning algorithms in constructing SDMs for the endangered aquatic species.