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Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution

التفاصيل البيبلوغرافية
العنوان: Spatial Evaluation of Machine Learning-Based Species Distribution Models for Prediction of Invasive Ant Species Distribution
المؤلفون: Wang-Hee Lee, Jae-Woo Song, Sun-Hee Yoon, Jae-Min Jung
المصدر: Applied Sciences, Vol 12, Iss 20, p 10260 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: Anoplolepis gracilipes, artificial intelligence, climate change, Solenopsis invicta, species distribution modeling, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of Solenopsis invicta would be expanded by climatic change, whereas it would not significantly change for Anoplolepis gracilipes. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/12/20/10260; https://doaj.org/toc/2076-3417
DOI: 10.3390/app122010260
URL الوصول: https://doaj.org/article/7994db1f962d441dad9015f65e0ddd4d
رقم الأكسشن: edsdoj.7994db1f962d441dad9015f65e0ddd4d
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app122010260