在不同空间建模条件下对Corythucha marmorata分布的全球和区域评估
Dae-Hyeon Byeon1, Wang-Hee Lee2,3
1Department of Smart Agriculture systems Machinery Engineering, Chungnam National University, Daejeon, 34134, South Korea.
Scientific reports
|March 13, 2026
概括
选择最好的物种分布模型是准确生态预测的关键. 这项研究对蕾丝虫的模型进行了优化,发现组合方法与特定数据技术在韩国表现最好.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 生物地理学 生物地理学 生物地理学
背景情况:
- 物种分布模型 (SDM) 对于了解物种生态至关重要.
- 模型性能对算法选择和数据特征敏感,需要仔细选择.
- 通过使用集成建模和适当的数据预处理,可以减少SDM中的不确定性.
研究的目的:
- 评估各种基于算法的单个和整体模型,以预测Corythucha marmorata (花蕾丝虫) 的全球息地适应性.
- 评估不同伪缺席数据生成方法对模型性能的影响.
- 应用优化的模型来预测C. marmorata在韩国的潜在分布.
主要方法:
- 利用多个基于算法的单个模型进行全球息地适宜性预测.
- 开发了使用平均值,中位数,委员会平均值和加权平均值方法的整体模型.
- 测试了伪缺席数据生成方法 (随机,表面范围信封,磁盘) 与组合模型一起.
主要成果:
- 集成模型,特别是委员会平均值和加权平均值与表面范围信封方法,实现了高性能 (TSS 0.980和0.977).
- 预测韩国各地出现C. marmorata的可能性很高,但最南端的岛屿除外.
- 证明了将集合建模与特定数据预处理技术相结合的有效性.
结论:
- 该研究提供了对优化物种分布建模方法的见解.
- 突出了算法选择,组合技术和数据预处理对于准确预测的重要性.
- 提供了韩国C. marmorata分布的可靠预测,为潜在的管理策略提供了信息.
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