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首页> 外文期刊>ICES Journal of Marine Science >Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics
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Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics

机译：断面数据的贝叶斯逻辑混合效应建模：将红树珊瑚的存在与栖息地特征联系起来

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摘要

The collection of continuous data on transects is a common practice in habitat and fishery stock assessments; however, the application of standard regression models that assume independence to serially correlated data is problematic. We show that generalized linear mixed models (GLMMs), i.e. generalized linear models for longitudinal data, that are normally used for studies performed over time can also be applied to other types of clustered or serially correlated data. We apply a specific GLMM for longitudinal data, a hierarchical Bayesian logistic mixed-effects model (BLMM), to a marine ecology dataset obtained from submersible video recordings of the seabed on transects at two sites in the Gulf of Alaska. The BLMM was effective in relating the presence of red tree corals (Primnoa pacifica; i.e. binary data) to habitat characteristics: the presence of red tree corals is highly associated with bedrock as the primary substrate (estimated odds ratio 9-19), high to very high seabed roughness (estimated odds ratio 3-5), and medium to high slope (estimated odds ratio 2-3). The covariate depth was less important at the sites. We also demonstrate and compare two methods of model checking: full and mixed posterior predictive assessments, the latter of which provided a more realistic assessment, and we calculate the variance partition coefficient for reporting the variation explained by multiple levels of the hierarchical model.
机译：连续采集断面数据是生境和渔业资源评估的一种普遍做法；但是，假设独立于序列相关数据的标准回归模型的应用存在问题。我们显示了通常用于随时间进行研究的广义线性混合模型（GLMM），即纵向数据的广义线性模型也可以应用于其他类型的聚类或序列相关数据。我们将特定的GLMM应用于纵向数据（分层贝叶斯逻辑混合效应模型（BLMM））应用于从阿拉斯加湾两个站点上样带上的海底潜水视频记录中获得的海洋生态数据集。 BLMM有效地将红树珊瑚（Primnoa pacifica；即二进制数据）的存在与栖息地特征相关联：红树珊瑚的存在与作为主要底物的基岩高度相关（估计比值比为9-19），高至非常高的海床粗糙度（估计比值比为3-5），以及中高坡度（估计比值比为2-3）。协变量深度在站点上不太重要。我们还演示并比较了两种模型检查方法：完全和混合后验预测评估，后者提供了更现实的评估，并且我们计算方差分配系数以报告由层次模型的多个级别解释的差异。

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