利用机器学习来发现跨共同分布的青家族的多病原体感染动态
Daniele L F Wiley1, Kadie N Omlor1, Ariadna S Torres López1
1Museum of Southwestern Biology, Department of Biology, University of New Mexico, Albuquerque, New Mexico, United States.
PeerJ
|February 3, 2025
概括
两动物的减少与病原体有关. 机器学习和历史样本揭示了宿主物种,较低的温度推动了Batrachochytrium dendrobatidis (Bd) 和Ranavirus (Rv) 的感染动态.
科学领域:
- 保护生物学 保护生物学
- 野生动物疾病生态学 野生动物疾病生态学
- 两病理学 两病理学
背景情况:
- 两动物种群面临着严重的下降,原因是新出现的传染病.
- 了解病原体动态是复杂的,受宿主,病原体和环境因素的影响.
- 传统的统计方法在广泛规模的多变量分析中扎.
研究的目的:
- 利用自然历史收藏和机器学习来分析两病原体动态.
- 确定三种一般性青病原体感染流行和强度的关键驱动因素.
- 评估宿主特征和环境变量对感染模式的影响.
主要方法:
- 从冷的自然历史收藏中,在美国东部采集了12个焦点两类标本.
- 使用qPCR对Batrachochytrium dendrobatidis (Bd),Ranavirus (Rv) 和两公海 (Pr) 的量化感染负载.
- 应用平衡随机森林 (RF) 模型,根据宿主和环境数据预测感染状态和强度.
主要成果:
- 大约20%的两动物被感染;Bd是最常见的 (16.9%),其次是Rv (4.38%) 和Pr (1.06%).
- 类动物家族的流行率和强度最高,特别是Rv和Bd.
- 主体物种和较高度的较冷,稳定的温度是Bd和Rv的关键预测因素;Pr趋势相反. 青少年的Rv和Bd负载比成年人高.
结论:
- 机器学习和广泛采样有效地识别了两动物感染的驱动因素.
- 主体分类学和温度是影响两动物病原体动态的关键因素.
- 通过突出疾病风险的关键生态相关性,研究结果为保护战略提供了信息.
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