利用高斯过程回归从时间序列数据中量化和探索状态依赖的生态相互作用。

IF 3.5 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Journal of The Royal Society Interface Pub Date : 2025-07-01 Epub Date: 2025-07-09 DOI:10.1098/rsif.2025.0154
Taiju Yukihira, Yutaka Osada, Michio Kondoh
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引用次数: 0

摘要

自然群落中的生态相互作用往往是高度非线性的;也就是说,相互作用的强度会随社区状态而暂时波动。从经验数据中推断状态依赖相互作用的有效和可靠的工具对生态学研究至关重要。在此,我们提出了一种基于高斯过程回归的非参数推理方法来量化非线性时间序列数据的相互作用强度。本文通过对生态学中高斯过程经验动态建模(GP-EDM)方法的扩展,介绍了该方法。为了验证其适用性,我们使用合成和实时序列数据研究了该方法的性能。结果表明,该方法具有几个明显的特点。首先,通过与现有方法(S-map和正则化S-map)的性能比较,本文方法对噪声时间序列数据具有更高的推理精度。其次,该方法分析了交互强度对社区状态的依赖关系。这使我们能够通过探索假设的社区状态来局部评估相互作用强度的状态依赖变化。此外,由于后验函数是解析导出的,因此该方法可以方便地评估推理的不确定性(如可信区间),从而获得更可靠的推理结果。提出的方法为解决物种相互作用分析中的状态依赖性提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantifying and exploring state-dependent ecological interactions from time series data using Gaussian process regression.

Ecological interactions in natural communities are often highly nonlinear; that is, interaction strengths can fluctuate temporally depending on community states. An effective and reliable tool to infer state-dependent interactions from empirical data is crucial to ecological studies. Here, we propose a novel non-parametric inference method based on Gaussian process regression to quantify interaction strengths from nonlinear time series data. We introduce the method by extending the Gaussian process empirical dynamic modelling (GP-EDM) approach in ecology. To confirm its applicability, we investigated the performance of the proposed method, using both synthetic and real-time series data. The results highlight that the proposed method possesses several distinct features. First, throughout performance comparison with existing methods (S-map and regularized S-map), the proposed method achieves higher inference accuracy for noisy time series data. Second, the proposed method analytically accounts for the dependence of interaction strengths on community states. This enables us to locally evaluate state-dependent changes in interaction strengths by exploring hypothetical community states. Moreover, because the posterior function is derived analytically, the proposed method can easily evaluate the inference uncertainty (e.g. credible interval), resulting in more reliable inference outcomes. The proposed method provides a basis for addressing state dependence in analyses of species interactions.

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来源期刊
Journal of The Royal Society Interface
Journal of The Royal Society Interface 综合性期刊-综合性期刊
CiteScore
7.10
自引率
2.60%
发文量
234
审稿时长
2.5 months
期刊介绍: J. R. Soc. Interface welcomes articles of high quality research at the interface of the physical and life sciences. It provides a high-quality forum to publish rapidly and interact across this boundary in two main ways: J. R. Soc. Interface publishes research applying chemistry, engineering, materials science, mathematics and physics to the biological and medical sciences; it also highlights discoveries in the life sciences of relevance to the physical sciences. Both sides of the interface are considered equally and it is one of the only journals to cover this exciting new territory. J. R. Soc. Interface welcomes contributions on a diverse range of topics, including but not limited to; biocomplexity, bioengineering, bioinformatics, biomaterials, biomechanics, bionanoscience, biophysics, chemical biology, computer science (as applied to the life sciences), medical physics, synthetic biology, systems biology, theoretical biology and tissue engineering.
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