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首页> 外文期刊>Journal of the Royal Society Interface >Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data
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Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data

机译：非线性时间序列数据群落动态生物交互对非线性时间序列数据的不确定性量化

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摘要

Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.
机译：预计生物互动将在塑造生态系统的动态方面发挥重要作用。然而，由于缺乏从实验数据中提取了准确测量的相互作用参数，量化生物相互作用的影响一直挑战。现有方法的主要局限之一是从嘈杂，稀疏采样，非线性数据推断的参数很少唯一可识别。也就是说，许多不同的参数可以与相同的数据集兼容，并且可以概括到独立数据。因此，难以证明关于生物相互作用的影响的决定性断言，但无需关于他们相关的不确定性的信息。在这里，我们开发基于模型平均的集合方法，以量化与非平衡生态时间序列数据的社区动态对社区动态的影响相关的不确定性。我们的方法能够检测多变量时间序列内的每个生物交互的最佳信息间隔，并且可以容易地适应不同的回归方案。总体而言，这种新方法可用于将时间依赖性不确定性与生物相互作用的影响联系起来。此外，由于我们量化了关于数据生成过程的最小假设的不确定性，所以我们的方法可以应用于变量之间的交互的任何数据强烈影响系统的整体动态。

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