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Short time series obscure compensatory dynamics in ecological communities

Nature Ecology & Evolution volume 9, pages 1405–1413 (2025)Cite this article

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Abstract

The degree of synchronous versus compensatory dynamics among species is crucial for determining the stability of ecological communities. Although robust quantification of species synchrony requires long-term observations, empirical studies are often based on short time series. Here we explore the effects of time series length on species synchrony by combining spectral analysis, dynamical community models and empirical plant community data. Our theoretical analyses show that competition contributes to decreasing species synchrony over long timescales but causes increases in synchrony over short timescales. As a result, species synchrony tends to decrease with time series length. In model communities, species synchrony calculated from long time series decreases with species diversity and competition, whereas that calculated from short time series increases with diversity and competition. Empirical analyses of >2,000 time series of plant communities support these theoretical predictions. Our analyses demonstrate that both species synchrony itself and its relationship with species richness can exhibit opposite patterns, depending on the length of time series, challenging the implicit assumption in ecological studies that observational length should not qualitatively alter patterns of interest. Our findings help reconcile results from theoretical and empirical studies on synchrony and have implications for sampling design.

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Fig. 1: Patterns of species synchrony in competition models.
Fig. 2: Relationships between species synchrony with species richness and competition strength.
Fig. 3: Relationships between species synchrony and time series length in observational and experimental data.
Fig. 4: Relationships between species synchrony and species diversity under different resampling strategies in Cedar Creek biodiversity experiments.

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Data availability

The analysed data and the codes to generate simulated data are available via figshare at https://figshare.com/s/52cd8c1a35f7cb8d801c (ref. 44).

Code availability

Numerical simulations were performed on Matlab2024b. Data analyses were performed on R (v.4.4.0) and linear mixed-effects models are analysed using R package lme4. Codes are available via figshare at https://figshare.com/s/52cd8c1a35f7cb8d801c (ref. 44).

Change history

  • 09 July 2025

    In the version of the article initially published, in the Acknowledgements, the National Key Research and Development Programme of China grant number was incorrect and has now been amended to 2022YFF0802103 in the HTML and PDF versions of the article.

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Acknowledgements

This work is supported by the National Key Research and Development Programme of China (2022YFF0802103) and the National Natural Science Foundation of China (32425036, 32588202) and is part of the Long-Term Ecological Research (LTER) Synchrony Synthesis Group funded by the National Science Foundation (US NSF) under grant no. DEB-1545288, through the LTER Network Communications Office and hosted at the National Center for Ecological Analysis and Synthesis. Cedar Creek data collection was supported by NSF LTER grant nos DEB-0620652, DEB-1234162 and DEB-1831944. D.R. was partly supported by US NCF grant nos 2023474 and 2414418 and the McDonnell and Humboldt foundations. L.S. was supported by NSF grant nos 2033292 and 2019528. P.B.R. was supported by NSF Long-Term Research in Environmental Biology (LTREB) grant nos DEB-1242531 and DEB-1753859; Ecosystem Sciences grant no. DEB-1120064; Biocomplexity grant no. DEB-0322057 and the ASCEND Biological Integration Institutes grant no. US NSF-DBI-2021898. M. Loreau was supported by the TULIP Laboratory of Excellence (ANR-10-LABX-41).

Author information

Authors and Affiliations

  1. Institute of Ecology, College of Urban and Environmental Sciences, and State Key Laboratory for Vegetation Structure, Function and Construction (VegLab), Peking University, Beijing, China

    Mingyu Luo, Michel Loreau & Shaopeng Wang

  2. Environmental Studies Program and Department of Biology, University of Oregon, Eugene, OR, USA

    Lauren M. Hallett

  3. Department of Ecology and Evolutionary Biology and Center for Ecological Research, University of Kansas, Lawrence, KS, USA

    Daniel C. Reuman

  4. Botany Department, University of Wyoming, Laramie, WY, USA

    Lauren G. Shoemaker

  5. State Key Laboratory of Nutrient Use and Management, Beijing Key Laboratory of Biodiversity and Organic Farming, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China

    Lei Zhao

  6. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Lin Jiang

  7. Theoretical and Experimental Ecology Station, CNRS, Moulis, France

    Michel Loreau

  8. Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Peter B. Reich

  9. Department of Forest Resources, University of Minnesota, St Paul, MN, USA

    Peter B. Reich

  10. Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia

    Peter B. Reich

  11. Department of Ecology, Evolution and Behavior, University of Minnesota, Saint Paul, MN, USA

    David Tilman

  12. Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA

    David Tilman

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  1. Mingyu Luo
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Contributions

S.W. designed the research. M. Luo and S.W. performed the research. M. Luo derived analytic solutions and analysed the model and data. M. Luo, L.M.H., D.C.R., L.G.S., L.Z. and S.W. contributed new reagents/analytic tools. P.B.R. and D.T. curated the field data. M. Luo and S.W. wrote the first draft of the paper. L.M.H., D.C.R., L.G.S., L.Z., L.J., M. Loreau, P.B.R. and D.T. contributed substantially to revision.

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Correspondence to Shaopeng Wang.

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Nature Ecology & Evolution thanks Takehiro Sasaki, David Vasseur and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Effects of time series length on synchrony under different scenarios of environmental noises.

Effects of time series length on species synchrony in two-species symmetric Lotka-Volterra competition models under different scenarios of correlation in species environmental drivers (a) and temporal autocorrelation in environmental noise (b). Other parameters are same as in Fig. 2.

Extended Data Fig. 2 Effects of competition strength and species richness on species synchrony in short and long time series under different degrees of species correlation in environmental response.

Effects of competition strength (a,b) and species richness (c,d) on species synchrony in symmetric Lotka-Volterra competition models under different scenarios of correlation in species environmental response (ρ), in short (a,c) and long (b,d) time series. Other parameters are same as in Fig. 2.

Extended Data Fig. 3 Effects of competition strength and species richness on species synchrony in short and long time series under different degrees of temporal autocorrelation in environmental noise.

Effects of competition strength (a,b) and species richness (c,d) on species synchrony in two-species symmetric Lotka-Volterra competition models under different scenarios of temporal autocorrelation in environmental noise (q), in short (a,c) and long (b,d) time series. Other parameters are same as in Fig. 2.

Extended Data Fig. 4 Competition-driven synchrony over short timescales versus compensatory dynamics over long timescales.

(a) and (b) illustrates the responses of a two-species community to a pulse perturbation, with the blue and red curves representing the abundances of two species in a deterministic Lotka-Volterra competition model (that is without environmental noises; other parameters are same as for Fig. 2). Starting from equilibrium (both species at population size X* = 6.67), a perturbation occurs that suddenly increased (a) or decreased (b) the abundance of species 1. Taking (a) for example. Following the perturbation, the two species both decreased, leading to synchronous dynamics in the short term. These synchronous dynamics were followed by compensatory dynamics in the long term, where the species 2 begins to increase once the abundance of the species 1 recover to a certain level. (c) and (d) illustrates the responses of the community to continuous random perturbations (that is, environmental noises). In (c), the population dynamics of the two species are positively correlated at short terms (for example, grey areas) but negatively correlated at long terms. The numbers above the grey areas represented the correlation coefficients during the observational window. In (d), the population growth rates of the two species are positively correlated at both short and long terms.

Extended Data Fig. 5 Effects of time series length on two alternative synchrony metrics.

Effects of time series length on two alternative synchrony metrics: the mean pairwise correlation coefficients (a-c), and the LM synchrony metric developed by ref. 14 (b-d). (a,d) show the change in synchrony with increasing time series length. (b,c) show the relationship of synchrony with species richness, and (c,f) show the relationship of synchrony with competition strengths. In (a,d), competition strength = 0.5 and species richness = 2; in (b,e), species richness = 2; in (c,f), competition strength = 0.5. Other parameters are same as in Fig. 2.

Extended Data Fig. 6 The critical time series length for observing a negative regression slope between species diversity and synchrony.

Relationship between the critical time series length and the population growth rate, under different scenarios of experimental replicates (a) or competition strength (b). Based on our symmetric competition models, the critical time series length is determined as the minimum time series length with more than 80% chance of observing a negative regression slope between diversity and synchrony. In (a), a number of replicates (1 or 5) was simulated for each level of species richness (S = 2, 3, …, 10), and the synchrony-diversity relationships were constructed across all simulated communities under given time series length. In (b), results under two different competition strengths were presented. Other parameters are same as for Fig. 2.

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Luo, M., Hallett, L.M., Reuman, D.C. et al. Short time series obscure compensatory dynamics in ecological communities. Nat Ecol Evol 9, 1405–1413 (2025). https://doi.org/10.1038/s41559-025-02757-w

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