Spatial ensemble learning for predicting the potential geographical distribution of invasive species

Abstract

Understanding the geographical distribution of invasive species is beneficial for preventing and controlling biological invasions. A global model is often constructed with existing species distribution models (SDMs) to describe the relationships between environmental characteristics and species distributions. Because of the spatial variations in environmental characteristics, it may be difficult for a single SDM to obtain an accurate result in any given location or area. Therefore, a spatial ensemble learning method for predicting the potential geographical distribution of invasive species is presented in this study. The method mainly includes two types of learners: one learner is a base learner used to predict the geographical distribution of invasive species, and the other learner is a spatial ensemble learner for combining predictions from different base learners. In this research, spatial ensemble learning is used to predict the geographical distribution of Erigeron annuus in the Yangtze River Economic Belt, China. The kappa coefficient and AUC (area under the receiver operating characteristic curve) obtained with the spatial ensemble learner are 0.88 and 0.94, respectively, and these values are greater than those obtained using three base learners and other ensemble strategies. This demonstrates the feasibility and effectiveness of spatial ensemble learning.

Keywords:

• Species distribution
• biological invasions
• spatial prediction
• machine learning
• ensemble learning

Acknowledgments

The authors would like to deeply appreciate the editor and the reviewer for their helpful comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and code availability statement

The data and code for the method that supports the findings of this study are available in Figshare at https://doi.org/10.6084/m9.figshare.24847452.

Additional information

Funding

This study was supported by the Scientific Research Fund of Hunan Provincial Education Department (23B0474).

Notes on contributors

Wentao Yang

Wentao Yang is currently an associate professor at Hunan University of Science and Technology. His research interests focus on spatio-temporal data mining and sustainable development goals monitoring. He contributed to coming up with the method, processing the data, coding methods, performing experiments, and writing.

Xiafan Wan

Xiafan Wan received the B.S. degree in geographical information science from Hunan University of Science and Technology in 2024. Her research interests focus on spatio-temporal prediction and ecological modelling. She contributed to processing the data, coding methods, performing experiments, writing, and revising the paper.

Min Deng

Min Deng is currently a professor at Central South University and the associate dean of the School of Geosciences and Info-Physics. His research interests are map generalization, spatio-temporal data analysis and mining. He contributed to refining the proposed method and the discussion of the findings.