ROSS, Chandler; LAITINEN, Ville; KHALIGHI, Moein; SALOJÄRVI, Jarkko; DE VOS, Willem; SOMMERIA-KLEIN, Guilhem; LAHTI, Leo

doi:10.48550/arXiv.2501.03820

Métadonnées

Afficher la notice complète

Licence d’utilisation du document

ROSS, Chandler
University of Turku

LAITINEN, Ville
University of Turku

KHALIGHI, Moein
University of Turku

Langue

Document de travail - Pré-publication

Ce document a été publié dans

2025

Résumé en anglais

Ecosystems tend to fluctuate around stable equilibria in response to internal dynamics and environmental factors. Occasionally, they enter an unstable tipping region and collapse into an alternative stable state. Our understanding of how ecological communities vary over time and respond to perturbations depends on our ability to quantify and predict these dynamics. However, the scarcity of long, dense time series data poses a severe bottleneck for characterising community dynamics using existing methods. We overcome this limitation by combining information across multiple short time series using Bayesian inference. By decomposing dynamics into deterministic and stochastic components using Gaussian process priors, we predict stable and tipping regions along the community landscape and quantify resilience while addressing uncertainty. After validation with simulated and real ecological time series, we use the model to question common assumptions underlying classical potential analysis and re-evaluate the stability of previously proposed "tipping elements" in the human gut microbiota.< Réduire

Mots clés en anglais

Bistability

exit time

Gaussian processes

human gut microbiota

microbial ecology

stability landscape

stochastic differential equation

tipping points

Métadonnées

Licence d’utilisation du document

Reconstructing community dynamics from limited observations

Langue

Ce document a été publié dans

Résumé en anglais

Mots clés en anglais

DOI

Origine

Unités de recherche