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首页> 外文期刊>Ecosystems >Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation?
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Spatio-Temporal Structural Equation Modeling in a Hierarchical Bayesian Framework: What Controls Wet Heathland Vegetation?

机译:分层贝叶斯框架中的时空结构方程建模:控制湿芙州植被的哪些控制?

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摘要

Structural equation models are highly suited for evaluating ecosystem-level hypotheses, but to be effective, structural equation models need to be able to accommodate spatial and temporal data. Here, the importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatio-temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathland plots, were sampled in the period 2007-2014. Including resampling over the years, 1322 plots were sampled. Plant cover was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, Molinia caerulea, and an aggregate class of other higher plants was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing. Generally, the two dwarf shrubs, E. tetralix and C. vulgaris, responded in qualitatively similar ways to the abiotic variables and qualitatively oppositely to the way the grass M. caerulea and the aggregate class of other higher plants. Furthermore, the spatial effects were qualitatively similar to the temporal effects. The two dwarf shrub species were most likely positively affected by nitrogen deposition, soil pH, sandy soils, low precipitation, and the absence of grazing. The study demonstrated that important insight on ecosystem dynamics and regulation can be obtained by spatial and temporal structural equation modeling in a hierarchical Bayesian framework and that the proper statistical modeling of the joint species abundance is a key feature of such models. Furthermore, the advantages of partitioning different types of uncertainties become clear when the fitted structural equation model is used for predictive purposes at a specific site.
机译:结构方程模型非常适合评估生态系统级假设,而是有效的,结构方程模型需要能够容纳空间和时间数据。在这里,使用分级贝叶斯框架中的时空结构方程模型研究了不同非生物和生物驱动器对湿芙划植被的重要性。来自39个丹麦地点的生态数据,每次有几个潮湿的Heathland地块,在2007 - 2014年期间被取样。多年来,包括重采样,取样1322个地块。使用PIN点法测量植物盖,据估计Dirichlet-Multinial混合物估计了湿Heathland生态系统中,湿芙蓉生态系统中的关键植物物种的关节分布,莫里尼亚·塞拉夏和其他高等植物的聚集等类别分配。湿芙州植被的调查司机包括氮沉积,土壤型,pH,降水和放牧。通常,两种矮灌木,E.Tetralix和C.Ventgaris,以与非生物变量的定性相似的方式作出质疑相反,以草地敏感和其他高等植物的聚集类别相反。此外,空间效应与时间效应类似地类似。两种侏儒灌木物种最可能受氮沉积,土壤pH,砂土,低沉淀和缺乏放牧的影响。该研究表明,通过分层贝叶斯框架中的空间和时间结构方程建模可以获得对生态系统动力学和调节的重要见解,并且关节物种丰富的适当统计建模是这种模型的关键特征。此外,当装配的结构方程模型用于特定部位的预测目的时,将不同类型的不确定性分配不同类型的不确定性的优点。

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