ArticleEcology letters2023
Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics.
Article in Ecology letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Transient dynamics and nonlinear fitness: A matrix approach to pulse and press perturbation.Ecology · 2026Article
- Linking Biotic Interactions to Species Stability.Ecology letters · 2026Article
- Revealing unseen dynamical regimes of ecosystems from population time-series data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The impact of data resolution on dynamic causal inference in multiscale ecological networks.Communications biology · 2024Article
- How biotic interactions structure species' responses to perturbations.Proceedings. Biological sciences · 2024Article
- Anticipating regime shifts by mixing early warning signals from different nodes.Nature communications · 2024Article
- Early warning signals have limited applicability to empirical lake data.Nature communications · 2023Article
- Ranking species based on sensitivity to perturbations under non-equilibrium community dynamics.Ecology letters · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Managing ecological communities requires fast detection of species that are sensitive to perturbations. Yet, the focus on recovery to equilibrium has prevented us from assessing species responses to perturbations when abundances fluctuate over time. Here, we introduce two data-driven approaches (expected sensitivity and eigenvector rankings) based on the time-varying Jacobian matrix to rank species over time according to their sensitivity to perturbations on abundances. Using several population dynamics models, we demonstrate that we can infer these rankings from time-series data to predict the order of species sensitivities. We find that the most sensitive species are not always the ones with the most rapidly changing or lowest abundance, which are typical criteria used to monitor populations. Finally, using two empirical time series, we show that sensitive species tend to be harder to forecast. Our results suggest that incorporating information on species interactions can improve how we manage communities out of equilibrium.
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Registered trials
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