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首页> 外文期刊>Environmetrics >Statistical inference for food webs with emphasis on ecological networks via Bayesian melding
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Statistical inference for food webs with emphasis on ecological networks via Bayesian melding

机译：通过贝叶斯融合对食物网进行统计推断，重点是生态网络

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摘要

Quantifying activities in a food web or ecological network, and related aspects of dependence, has largely been either descriptive or deterministic. Although schemes exist for assessing the reliability of such quantification, many are far from being statistical in nature. Statistical modeling approaches are explored, with a focus on the ecosystem aspects of a food web. By employing Bayesian melding, we provide a new statistical inferential approach for understanding ecological networks in the context of mass balance. Our approach embodies the traditional deterministic views on network relations, yet it is developed on the basis of proper statistical inference that allows the estimation of physical quantities and probabilistic assessment of the estimation. We describe our approach, and illustrate it with a mass balance dataset. The practical advantage of our approach is a more realistic understanding of the network by incorporating natural measurement variability into deterministic beliefs about the relationships among measurements. The resulting inference thus forms a more honest representation of the true state of nature, and provides a formal assessment of balance before data are passed on to later stages of an ecological network analysis (ENA). We also demonstrate that general Bayesian inference for ENA can yield new ecological insight that may not be available through standard classical inference.
机译：量化食物网或生态网络中的活动以及相关性的相关方面在很大程度上是描述性或确定性的。尽管存在用于评估这种量化的可靠性的方案，但许多方案本质上还远非统计。探索了统计建模方法，重点是食物网的生态系统方面。通过采用贝叶斯融合，我们提供了一种新的统计推论方法，用于理解质量平衡背景下的生态网络。我们的方法体现了关于网络关系的传统确定性观点，但是它是在适当的统计推断的基础上开发的，该统计推断允许对物理量进行估计并对该估计进行概率评估。我们描述了我们的方法，并用质量平衡数据集进行了说明。通过将自然的测量可变性纳入有关测量之间关系的确定性信念中，我们方法的实际优势是对网络有了更现实的理解。因此，得出的推论形成了真实的自然状态的更真实的表示，并在将数据传递到生态网络分析（ENA）的后续阶段之前提供了对平衡的正式评估。我们还证明，对ENA的一般贝叶斯推论可以产生新的生态学见识，而标准的经典推论可能无法提供这种新的生态学见识。

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