Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model
Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model
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DOI：
10.1023/a:1026001008598
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发表时间：
2003-01-01
影响因子：
5.2
通讯作者：
Wikle, CK
中科院分区：
文献类型：
--
作者：
Hooten, MB;Larsen, DR;Wikle, CK
适应生态模型中的重要不确定性来源对于现实地预测生态过程是至关重要的。该项目的目的是开发一种可靠的方法来模拟景观上的自然过程，同时通过利用环境和空间随机效应来考虑过程中的可变性。层次化的贝叶斯框架允许同时整合这些影响。这个框架自然假设变量是随机的，模型的后验分布提供了关于该过程的概率信息。以美国密苏里州东南部的两种金线莲为例，说明了该模型的实用性。此外，两种验证技术被用来评估预测的定性和定量特征。
Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural processes on a landscape while accounting for the variability in a process by utilizing environmental and spatial random effects. A hierarchical Bayesian framework has allowed the simultaneous integration of these effects. This framework naturally assumes variables to be random and the posterior distribution of the model provides probabilistic information about the process. Two species in the genus Desmodium were used as examples to illustrate the utility of the model in Southeast Missouri, USA. In addition, two validation techniques were applied to evaluate the qualitative and quantitative characteristics of the predictions.