Energy landscape analysis elucidates the multistability of ecological communities across environmental gradients

Suzuki, Kenta; Nakaoka, Shinji; Fukuda, Shinji; Masuya, Hiroshi

doi:10.1002/ecm.1469

Cited by 19 publications

(83 citation statements)

References 114 publications

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“…Génin et al 2020), or through building weighted networks of states calculated by pairwise maximum entropy models(Suzuki et al 2021). Other methods attempt to reconstruct the parameter space where bistability exists by fitting the simplest ('normal') form function (similar to equation (…”

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confidence: 99%

Ecological resilience: what to measure and how

Dakos

Kéfi

2022

Environ. Res. Lett.

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The question of what and how to measure ecological resilience has been troubling ecologists since Holling 1973's seminal paper in which he defined resilience as the ability of a system to withstand perturbations without shifting to a different state. This definition moved the focus from studying the local stability of a single attractor to which a system always converges, to the idea that a system may converge to different states when perturbed. These two concepts have later on led to the definitions of engineering (local stability) vs ecological (non-local stability) resilience metrics. While engineering resilience is associated to clear metrics, measuring ecological resilience has remained elusive. As a result, the two notions have been studied largely independently from one another and although several attempts have been devoted to mapping them together in some kind of coherent framework, the extent to which they overlap or complement each other in quantifying the resilience of a system is not yet fully understood. In this perspective, we focus on metrics that quantify resilience following Holling's definition based on the concept of a stability landscape. We explore the relationships between different engineering and ecological resilience metrics derived from bistable systems and show that, for low dimensional ecological models, the correlation between engineering and ecological resilience can be high. We also review current approaches for measuring resilience from models and data, and we outline challenges which, if answered, could help us make progress towards a more reliable quantification of resilience in practice.

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confidence: 99%

Ecological resilience: what to measure and how

Dakos

Kéfi

2022

Environ. Res. Lett.

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“…3a): this index represents how current community states are inflated from local optima (i.e., "bottom" of basins). The other is "stable-state entropy 24 ", which is calculated based on the random-walk-based simulation from current community states to bottoms of any energy landscape basins (i.e., alternative stable states). A starting community state is inferred to have high entropy if reached stable states are variable among random-walk iterations: the stable-state entropy is defined as the Shannon's entropy of the final destinations of the random walk 24 .…”

Section: Anticipating Abrupt Community Shiftsmentioning

confidence: 99%

“…The other is "stable-state entropy 24 ", which is calculated based on the random-walk-based simulation from current community states to bottoms of any energy landscape basins (i.e., alternative stable states). A starting community state is inferred to have high entropy if reached stable states are variable among random-walk iterations: the stable-state entropy is defined as the Shannon's entropy of the final destinations of the random walk 24 . Therefore, communities approaching abrupt structural changes are expected to have high stable-state entropy because they are inferred to cross over "ridges" on energy landscapes.…”

Section: Anticipating Abrupt Community Shiftsmentioning

confidence: 99%

“…1a). One is the framework of energy landscape analyses in statistical physics 24–26 , in which stability/instability of possible community states (compositions) are evaluated in the metric of “energy”. In energy landscape analyses, stable states within a state space are defined as community compositions whose energy values are lower than those of adjacent community compositions 24 .…”

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confidence: 99%

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Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

Fujita

Ushio

Suzuki

et al. 2022

Preprint

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Microbiome dynamics are both crucial indicators and drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as dysbiosis in human microbiomes. We here integrate theoretical and empirical bases for anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We then confirmed that the abrupt community changes observed through the time-series could be described as shifts between alternative stable states or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the energy landscape analysis of statistical physics or that of a stability index of nonlinear mechanics. These results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems.

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“…(iv) We are also able to emulate assembly dynamics constrained on the energy landscape. Figure adapted from Suzuki et al (2020)…”

Section: Community Assembly Dynamics: Compositional Variabilitymentioning

confidence: 99%

Illuminating the intrinsic and extrinsic drivers of ecological stability across scales

Ross

Suzuki

Kondo

et al. 2021

Ecological Research

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Our knowledge of ecological stability is built on assumptions of scale. These assumptions limit our ability to reach a generalizable and mechanistic understanding of stability under global environmental change. Moving towards a multiscale approach—across space, time and environment—will allow us to better understand the intrinsic (e.g., demographic) and extrinsic (environmental) drivers of ecological stability. In this perspective, we review multiple sources of variation responsible for shaping ecological dynamics, and how scale affects our observation of these dynamics through its confounding effect on drivers of variation in ecosystems. We discuss the effect of temporal scale when combining empirical dynamic modeling with high‐resolution population time series to consider the time‐varying nature of multispecies interaction networks, highlighting interspecific interactions as an intrinsic driver of community dynamics. Next, we examine energy landscape analysis as a method for inferring stability and transience during community assembly and its interaction with spatial scale, emphasizing the intrinsic role of compositional variability in assembly dynamics. We then examine population dynamics at species' range margins and show how considering the interaction between spatial and temporal environmental heterogeneity, an extrinsic driver of population dynamics, can facilitate a nuanced understanding of population expansions, range shifts, and species invasions. Finally, we discuss broadly how the sources of intrinsic and extrinsic variation interact with each other and with spatiotemporal scale to shape ecological dynamics. Better recognition of the scale‐dependent nature of the relationship between drivers of variation and ecological dynamics will be invaluable to illuminate the dynamics influencing ecological stability across scales.

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Energy landscape analysis elucidates the multistability of ecological communities across environmental gradients

Cited by 19 publications

References 114 publications

Ecological resilience: what to measure and how

Ecological resilience: what to measure and how

Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

Illuminating the intrinsic and extrinsic drivers of ecological stability across scales

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