Quantitatively Inferring Three Mechanisms from the Spatiotemporal Patterns

Quantitatively Inferring Three Mechanisms from the Spatiotemporal Patterns
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从时空格局定量推断三种机制

DOI:
10.3390/math8010112
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发表时间:
2020-01
期刊:
影响因子:
2.4
通讯作者:
Liu Quan-Xing
Liu Quan-Xing
中科院分区:
数学3区
文献类型:
--
作者:
Zhang Kang;Hu Wen-Si;Liu Quan-Xing

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生态系统空间格局的多样性已引起人们的广泛关注,但其内在的生态过程和机制仍是一个挑战。偏微分方程(PDE)等动力学系统模型是揭示空间格局形成、探索潜在生态过程和机制的最常用框架。本文通过比较Allen-Cahn(AC)模型、Cahn-Hilliard(CH)模型以及Cahn-Hilliard与人口统计学(CHPD)模型模式化动力学的相似性,发现结构因子、密度涨落标度、Lifshitz-Slyozov(LS)标度和饱和状态的综合时空行为是推断潜在生态过程的有用指标,尽管它们显示出难以区分的空间模式。首先,CH模型和CHPD模型的结构因子有一个显著的峰值,而AC模型没有。其次,CH和CHPD模型分别在-2.90和-2.60的尺度下显示出超均匀行为,但AC模型在-1.91的尺度下显示出随机分布。第三,AC和CH都表现出均匀的LS行为,尺度分别为0.37和0.32,但CHPD模型在短时间尺度上的尺度为0.19,在长时间尺度上饱和。总之,我们提供的见解的动态指标/行为的空间格局,从纯空间数据和时空相关的数据,并推断生态过程的潜在应用。
Although the diversity of spatial patterns has gained extensive attention on ecosystems, it is still a challenge to discern the underlying ecological processes and mechanisms. Dynamical system models, such partial differential equations (PDEs), are some of the most widely used frameworks to unravel the spatial pattern formation, and to explore the potential ecological processes and mechanisms. Here, comparing the similarity of patterned dynamics among Allen–Cahn (AC) model, Cahn–Hilliard (CH) model, and Cahn–Hilliard with population demographics (CHPD) model, we show that integrated spatiotemporal behaviors of the structure factors, the density-fluctuation scaling, the Lifshitz–Slyozov (LS) scaling, and the saturation status are useful indicators to infer the underlying ecological processes, even though they display the indistinguishable spatial patterns. First, there is a remarkable peak of structure factors of the CH model and CHPD model, but absent in AC model. Second, both CH and CHPD models reveal a hyperuniform behavior with scaling of −2.90 and −2.60, respectively, but AC model displays a random distribution with scaling of −1.91. Third, both AC and CH display uniform LS behaviors with slightly different scaling of 0.37 and 0.32, respectively, but CHPD model has scaling of 0.19 at short-time scales and saturation at long-time scales. In sum, we provide insights into the dynamical indicators/behaviors of spatial patterns, obtained from pure spatial data and spatiotemporal related data, and a potential application to infer ecological processes.
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