A Bayesian multinomial Gaussian response model for organism-based environmental reconstruction
A Bayesian multinomial Gaussian response model for organism-based environmental reconstruction
复制标题
基于生物体的环境重建的贝叶斯多项高斯响应模型
DOI：
10.1023/a:1008180500301
复制
发表时间：
2000
影响因子：
2.1
通讯作者：
A. Korhola
中科院分区：
文献类型：
--
作者：
Kari Vasko;Hannu (TT) Toivonen;A. Korhola
提出了一种基于生物体的定量古环境重建的贝叶斯多项式回归模型(Bummer)。该模型基于经典的(直接)校准方法和仔细的统计环境建模，考虑了物种之间的统计相关性。我们将我们的贝叶斯模型Bummer与其他七种方法进行了比较，包括广泛使用的加权平均(WA)技术和我们之前的贝叶斯模型Bum。通过比较不同模型的交叉验证预测统计量，在北极北部62个亚北极湖泊的表层沉积物摇摆学训练集上对该方法进行了评估。BUMMER模型的预测误差最小，偏差最小，相关系数最大。我们认为，贝叶斯多项式高斯响应模型的良好性能源于以下原因：(1)考虑了特定地点潜变量的不确定性；(2)在模型描述中嵌入了生态背景知识；(3)将物种组成作为一个整体进行考虑；(4)基于经典的校正方法进行重建。
We present a Bayesian hierarchical multinomial regression model (Bummer) for organism-based quantitative paleoenvironmental reconstruction. The model is based on the classical (direct) approach to calibration and on careful statistical environmental modeling that takes account of statistical dependencies among species.We compare our Bayesian model Bummer to seven other methods, including the widely used weighted averaging (WA) techniques and our previous Bayesian model Bum. The methods are evaluated on a surface-sediment chironomid training set of 62 subarctic lakes in northern Fennoscandia by comparing the cross-validation prediction statistics of different models. Bummer outperformed other methods, yielding the smallest prediction error, the smallest bias, and the largest correlation coefficient.We conclude that the promising performance of our Bayesian multinomial Gaussian response model is due to the following reasons: (i) the uncertainty concerning site specific latent variables is taken into consideration; (ii) ecological background knowledge is embedded to the model description; (iii) the species compositions are considered as a whole; and (iv) reconstruction is based on the classical approach to calibration.