一个共同的模型用于从点引用数据估计物种分布和环境特征
Markus Viljanen1, Lisa Tostrams2, Niels Schoffelen2
1Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment, Bilthoven, Utrecht, The Netherlands.
PloS one
|June 21, 2024
概括
准确预测物种的出现需要考虑环境数据的不确定性. 一个新的贝叶斯模型改善了解释,特别是对于稀有物种,尽管标准模型足以进行一般预测.
科学领域:
- 生态生态学 生态生态学
- 保护生物学 保护生物学
- 统计建模 统计建模
背景情况:
- 物种分布模型 (SDM) 对于保护至关重要,它可以根据环境因素预测物种的出现.
- SDM中的环境数据经常被推断,引入了通常不考虑的不确定性.
- 独立变量的这种不确定性可能会影响SDM预测和解释的可靠性.
研究的目的:
- 开发和评估一个共同的分层贝叶斯模型物种分布.
- 正确地将环境变量中的不确定性传播到SDM中.
- 将拟议的模型与荷兰使用植物物种的标准方法进行比较.
主要方法:
- 实施了一种联合的等级贝叶斯模型,将环境变量模型直接集成到物种分布模型中.
- 该模型仅使用点参照观测进行了拟合,确保了适当的不确定性传播.
- 一个案例研究涉及50种荷兰植物物种和8种土壤状况预测因素,将结果与标准SDM方法进行比较.
主要成果:
- 新的贝叶斯方法显示,与标准模型相比,估计的物种-环境关联 (相关性0.64-0.84) 有差异.
- 预测的分布图在方法之间很大程度上相似 (相关性为0.82-1.00).
- 新模型为5种物种提供了更好的交叉验证准确性,并改善了解释,特别是在稀有物种.
结论:
- 标准的SDM通常适用于预测任务.
- 为了对物种与环境关系进行可靠的解释,建议采用包含环境预测因子的不确定性模型.
- 开发的贝叶斯框架提供了一种更准确的方法来处理SDM中的环境数据不确定性.
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