Non-parametric estimation of the structural stability of non-equilibrium community dynamics

Cenci, Simone; Saavedra, Serguei

doi:10.1038/s41559-019-0879-1

Cited by 35 publications

(122 citation statements)

References 36 publications

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“…To understand the persistence of a species in an ecological community, it is important (although not necessary, see Sugihara, 1994; Cenci and Saavedra, 2019) to have tractable (mechanistic or phenomenological) population dynamics models upon which one can study cause–effect relationships between model parameters and model outputs (Case, 2000; Strogatz, 2014). The stochastic nature of ecological dynamics can then be incorporated through either random noise or systematic changes of parameter values (Turchin, 2003; Schreiber et al ., 2019; Yang et al ., 2019).…”

Section: Understanding Species Persistencementioning

confidence: 99%

“…Indeed, apart from collecting an enormous amount of data (which may not be possible to obtain under time and resource constraints) and building sophisticated learning algorithms (which may not generalise well to unseen data nor provide ecological understanding), how to understand and predict the persistence of species subject to changing environments remains an open question (Sugihara et al ., 2012; Harfoot et al ., 2014; Dietze, 2017; Cenci and Saavedra, 2019). Yet, having a framework that could unify the problems of understanding and predicting can help us to answer not only the question of what is the probability of persistence of a species, but also the question of why is this probability small or large.…”

Section: Introductionmentioning

confidence: 99%

“…This field is providing the opportunity to anticipate rather than simply explain biodiversity changes in ecological communities contingent on explicit scenarios for climate change, land‐use and species re‐distributions (Dietze, 2017). Importantly, biodiversity forecasting spans and integrates many model‐driven (parametric) and data‐driven (nonparametric) methodologies, such as uncertainty propagation, statistics, informatics, Bayesian approaches, machine learning, Markov chain approaches, empirical dynamic modelling (Sugihara et al ., 2012; Harfoot et al ., 2014; Cazelles et al ., 2016; Dietze, 2017; Cenci and Saavedra, 2019; Adams et al ., 2020; Maynard et al ., 2020), as well as parameterising complex mechanistic models using either demographic, eco‐physiological or allometric information (Preston, 1962; Pacala et al ., 1996; Dietze, 2017). However, the majority of these methodologies demands extensive amounts of data, their explanatory power has been contested, and their generalisation has not always been validated with experimental work (Dietze, 2017; Clark et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Structural forecasting of species persistence under changing environments

2020

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The persistence of a species in a given place not only depends on its intrinsic capacity to consume and transform resources into offspring, but also on how changing environmental conditions affect its growth rate. However, the complexity of factors has typically taken us to choose between understanding and predicting the persistence of species. To tackle this limitation, we propose a probabilistic approach rooted on the statistical concepts of ensemble theory applied to statistical mechanics and on the mathematical concepts of structural stability applied to population dynamics modelswhat we call structural forecasting. We show how this new approach allows us to estimate a probability of persistence for single species in local communities; to understand and interpret this probability conditional on the information we have concerning a system; and to provide out-of-sample predictions of species persistence as good as the best experimental approaches without the need of extensive amounts of data.

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Section: Understanding Species Persistencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structural forecasting of species persistence under changing environments

2020

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“…A common approach to finding a middle way in the construction of early warning signs is to use data assimilation approaches to fit and/or continuously improve a dynamical model [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

et al. 2021

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The Jacobian matrix of a dynamical system describes its response to perturbations. Conversely, one can estimate the Jacobian matrix by carefully monitoring how the system responds to environmental noise. We present a closed-form analytical solution for the calculation of a system’s Jacobian from a time series. Being able to access the Jacobian enables a broad range of mathematical analyses by which deeper insights into the system can be gained. Here we consider in particular the computation of the leading Jacobian eigenvalue as an early warning signal for critical transitions. To illustrate this approach, we apply it to ecological meta-foodweb models, which are strongly nonlinear dynamical multi-layer networks. Our analysis shows that accurate results can be obtained, although the data demand of the method is still high.

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“…Ecological research has always stipulated that the occurrence of species does not only depend on the biotic factors described in ecological communities, but also depends on the PLOS COMPUTATIONAL BIOLOGY abiotic factors established by the environment [22,23]. This explanation is rooted upon the general idea of environmental filtering-the environment is a major force shaping almost all ecological systems [1,24,25]. For example, temperature variability affects both species interactions [26,27] and network structures [21,28,29].…”

Section: Introductionmentioning

confidence: 99%

Telling ecological networks apart by their structure: An environment-dependent approach

Song

Saavedra

2020

PLoS Comput Biol

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The network architecture of an ecological community describes the structure of species interactions established in a given place and time. It has been suggested that this architecture presents unique features for each type of ecological interaction: e.g., nested and modular architectures would correspond to mutualistic and antagonistic interactions, respectively. Recently, Michalska-Smith and Allesina (2019) proposed a computational challenge to test whether it is indeed possible to differentiate ecological interactions based on network architecture. Contrary to the expectation, they found that this differentiation is practically impossible, moving the question to why it is not possible to differentiate ecological interactions based on their network architecture alone. Here, we show that this differentiation becomes possible by adding the local environmental information where the networks were sampled. We show that this can be explained by the fact that environmental conditions are a confounder of ecological interactions and network architecture. That is, the lack of association between network architecture and type of ecological interactions changes by conditioning on the local environmental conditions. Additionally, we find that environmental conditions are linked to the stability of ecological networks, but the direction of this effect depends on the type of interaction network. This suggests that the association between ecological interactions and network architectures exists, but cannot be fully understood without attention to the environmental conditions acting upon them.

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Non-parametric estimation of the structural stability of non-equilibrium community dynamics

Cited by 35 publications

References 36 publications

Structural forecasting of species persistence under changing environments

Structural forecasting of species persistence under changing environments

A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

Telling ecological networks apart by their structure: An environment-dependent approach

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