Integrative Biology Journals

JOURNAL OF FORESTRY RESEARCH ›› 2023, Vol. 34 ›› Issue (5): 1447-1462.DOI: 10.1007/s11676-023-01604-7

• Original Paper • Previous Articles     Next Articles

Pattern changes and early risk warning of Spartina alterniflora invasion: a study of mangrove-dominated wetlands in northeastern Fujian, China

Fangyi Wang1,2, Jiacheng Zhang1,2, Yan Cao1,2,3, Ren Wang4, Giri Kattel5,6,7, Dongjin He1,2,8,f, Weibin You1,2,g   

  1. 1 College of Forestry, Fujian Agriculture and Forestry University, 350002, Fuzhou, People’s Republic of China
    2 South Forest Resources and Environment Engineering Technology Research Center of Fujian Province, 350002, Fuzhou, People’s Republic of China
    3 College of Finance, Fujian Jiangxia University, 350002, Fuzhou, People’s Republic of China
    4 Fuding Forestry Bureau, 355200, Fuzhou, People’s Republic of China
    5 School of Geographical Sciences, Nanjing University of Information Science & Technology, 210044, Nanjing, People’s Republic of China
    6 Department of Infrastructure Engineering, University of Melbourne, 3010, Melbourne, Australia
    7 Department of Hydraulic Engineering, Tsinghua University, 100084, Beijing, People’s Republic of China
    8 Fujian Vocational College of Agriculture, 350002, Fuzhou, People’s Republic of China
  • Received:2022-09-14 Accepted:2022-12-07 Online:2024-10-16
  • Contact: Dongjin He, Weibin You

Abstract:

The exotic saltmarsh cordgrass, Spartina alterniflora (Loisel) Peterson & Saarela, is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature. The species has rapidly spread wildly across coastal wetlands, challenging resource managers for control of its further spread. An investigation of S. alterniflora invasion and associated ecological risk is urgent in China’s coastal wetlands. In this study, an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework. Predictions were made of ‘warning degrees’: zero warning and light, moderate, strong, and extreme warning, by developing a back propagation (BP) artificial neural network model for coastal wetlands in eastern Fujian Province. Our results suggest that S. alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions. An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas. Areas of light warning and extreme warning accounted for 43% and 7%, respectively, suggesting the BP neural network model is reliable prediction of the ecological risk of S. alterniflora invasion. The model predicts that distribution pattern of this invasive species will change little in the next 10 years. However, the invaded patches will become relatively more concentrated without warning predicted. We suggest that human factors such as land use activities may partially determine changes in warning degree. Our results emphasize that an early warning system for S. alterniflora invasion in China’s eastern coastal wetlands is significant, and comprehensive control measures are needed, particularly for K. candel beach.

Key words: Early warning system, Ecological risk, BP neural network model, Spartina alterniflora invasion, Kandelia candel beaches, Fujian, China