Stability of multi-layer ecosystems

Wang, Ye; Yang, Yilin; Li, Aming; Wang, Long

doi:10.1098/rsif.2022.0752

Cited by 9 publications

(2 citation statements)

References 56 publications

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“…Complex networks, consisting of nodes representing components within a system and edges indicating their connections or relationships, serve as a highly effective framework for modeling a wide variety of natural and engineered systems [1][2][3][4][5][6]. In the field of network science, a significant amount of effort has been invested in understanding, predicting and controlling the behavior of complex systems modeled as static networks, where links or connection patterns are fixed between nodes.…”

Section: Introductionmentioning

confidence: 99%

Detecting the driver nodes of temporal networks

Qin

Duan

2023

New J. Phys.

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Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.

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Section: Introductionmentioning

confidence: 99%

Detecting the driver nodes of temporal networks

Qin

Duan

2023

New J. Phys.

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“…Similar to the animal muscle, pulvinus (the 'plant muscle') has triggered lots of bioinspired devices with hydrogels (Xu et al 2021) or fluid pressure as the actuation source, with gas (Vasista and Tong 2012, Li and Wang 2015b, Li and Wang 2015a, Li and Wang 2016 or liquid osmosis (Piyasena et al 2009, Must et al 2019.…”

Section: Introduction: Drawing Inspiration From Nature For Versatile ...mentioning

confidence: 99%

Deformation and mechanics of a pulvinus-inspired material

Tadrist¹,

Mammadi²,

Diperi³

et al. 2022

Bioinspir. Biomim.

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Mimosa Pudica rapidly folds leaves when touched. Motion is created by pulvini, ``the plant muscles'' that allow plants to produce various complex motion. Plants rely on local control of the turgor pressure to create on-demand motion. In this paper, the mechanics of a cellular material inspired from pulvinus of Mimosa Pudica is studied. First, the manufacturing process of a cell-controllable material is described. Its deformation behaviour when pressured is tested, focussing on 3 pressure patterns of reference. The deformations are modelled based on the minimisation of elastic energy framework. Depending on pressurisation pattern and magnitude, reversible buckling-induced motion may occur.

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Reactivity of complex communities can be more important than stability

Yang,

Coyte,

Foster

et al. 2023

Nat Commun

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Understanding stability—whether a community will eventually return to its original state after a perturbation—is a major focus in the study of various complex systems, particularly complex ecosystems. Here, we challenge this focus, showing that short-term dynamics can be a better predictor of outcomes for complex ecosystems. Using random matrix theory, we study how complex ecosystems behave immediately after small perturbations. Our analyses show that many communities are expected to be ‘reactive’, whereby some perturbations will be amplified initially and generate a response that is directly opposite to that predicted by typical stability measures. In particular, we find reactivity is prevalent for complex communities of mixed interactions and for structured communities, which are both expected to be common in nature. Finally, we show that reactivity can be a better predictor of extinction risk than stability, particularly when communities face frequent perturbations, as is increasingly common. Our results suggest that, alongside stability, reactivity is a fundamental measure for assessing ecosystem health.

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Stability of multi-layer ecosystems

Cited by 9 publications

References 56 publications

Detecting the driver nodes of temporal networks

Detecting the driver nodes of temporal networks

Deformation and mechanics of a pulvinus-inspired material

Reactivity of complex communities can be more important than stability

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