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Temperature variation and life history mediate nonlinearity in fluctuations of marine fish populations worldwide

Nature Ecology & Evolution (2026)Cite this article

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Abstract

Nonlinear dynamics readily occur in natural ecosystems and can drive irregular population fluctuations through oscillations, chaos and alternative stable states. However, the effects of anthropogenic changes, such as to demography and the climate, on nonlinearity of population fluctuations are unknown. We evaluated the extent and magnitude of nonlinearity and its environmental and life history correlates in 243 recruitment and 266 spawner time series of 143 marine fish species, worldwide. Here we show that temperature variation amplifies nonlinearity in recruitment and spawner biomass, while life history mediates the degree of nonlinearity for the latter, dampening it in slow-lived species. Nonlinearity was shown by 81% of populations and correlated with the magnitude of fluctuations. These nonlinear dynamics were low dimensional and causally forced by temperature in 69% of populations with the probability of forcing increasing for recruits in variable-temperature environments and fast-lived spawners. Our results challenge assumptions of stable dynamics and sustainable yield common to fisheries management, and suggest that nonlinear fluctuations of fish populations are magnified by size-selective fisheries and environmental variability from global climate change.

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Fig. 1: Marine fish populations are governed by low-dimensional, nonlinear dynamics.
Fig. 2: Temperature variation and fast life history traits amplify nonlinearity in marine fishes.
Fig. 3: Embedding dimension and prediction skill are greater for species with slow life history traits.
Fig. 4: Widespread causal forcing of temperature on fish population dynamics.

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Data availability

The RAM LSAD26, NOAA Optimum Interpolation SST V2 High Resolution database28 and COBE-SST 2 database58 are publicly available. All analysed data are available via Zenodo at https://doi.org/10.5281/zenodo.17582260 (ref. 67).

Code availability

All code needed to reproduce our analyses is available via Zenodo at https://doi.org/10.5281/zenodo.17582260 (ref. 67).

References

  1. May, R. M. Simple mathematical models with very complicated dynamics. Nature 261, 459–467 (1976).

    Article  CAS  PubMed  Google Scholar 

  2. Sugihara, G. et al. Are exploited fish populations stable?. Proc. Natl Acad. Sci. USA 108, E1224–E1225 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rogers, T. L., Johnson, B. J. & Munch, S. B. Chaos is not rare in natural ecosystems. Nat. Ecol. Evol. 6, 1105–1111 (2022).

    Article  PubMed  Google Scholar 

  4. Benincà, E. et al. Chaos in a long-term experiment with a plankton community. Nature 451, 822–825 (2008).

    Article  PubMed  Google Scholar 

  5. Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Clark, T. J. & Luis, A. D. Nonlinear population dynamics are ubiquitous in animals. Nat. Ecol. Evol. 4, 75–81 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Dakos, V., Glaser, S. M., Hsieh, C. & Sugihara, G. Elevated nonlinearity as an indicator of shifts in the dynamics of populations under stress. J. R. Soc. Interface 14, 20160845 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).

    Article  CAS  PubMed  Google Scholar 

  9. Hsieh, C., Glaser, S. M., Lucas, A. J. & Sugihara, G. Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean. Nature 435, 336–340 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Berkeley, S. A., Hixon, M. A., Larson, R. J. & Love, M. S. Fisheries sustainability via protection of age structure and spatial distribution of fish populations. Fisheries 29, 23–32 (2004).

    Article  Google Scholar 

  11. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).

    Article  Google Scholar 

  12. Costantino, R. F., Desharnais, R. A., Cushing, J. M. & Dennis, B. Chaotic dynamics in an insect population. Science 275, 389–391 (1997).

    Article  CAS  PubMed  Google Scholar 

  13. Werner, J., Pietsch, T., Hilker, F. M. & Arndt, H. Intrinsic nonlinear dynamics drive single-species systems. Proc. Natl Acad. Sci. USA 119, e2209601119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Beddington, J. R. & May, R. M. Harvesting natural populations in a randomly fluctuating environment. Science 197, 463–465 (1977).

    Article  CAS  PubMed  Google Scholar 

  15. Hsieh, C. et al. Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862 (2006).

    Article  CAS  PubMed  Google Scholar 

  16. Dixon, P. A., Milicich, M. J. & Sugihara, G. Episodic fluctuations in larval supply. Science 283, 1528–1530 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Glaser, S. M. et al. Detecting and forecasting complex nonlinear dynamics in spatially structured catch-per-unit-effort time series for North Pacific albacore (Thunnus alalunga). Can. J. Fish. Aquat. Sci. 68, 400–412 (2011).

    Article  Google Scholar 

  18. Glaser, S. M. et al. Complex dynamics may limit prediction in marine fisheries. Fish Fish. 15, 616–633 (2014).

    Article  Google Scholar 

  19. Glaser, S. M., Ye, H. & Sugihara, G. A nonlinear, low data requirement model for producing spatially explicit fishery forecasts. Fish. Oceanogr. 23, 45–53 (2014).

    Article  Google Scholar 

  20. Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: a global meta-analysis. Fish Fish. 19, 964–973 (2018).

    Article  Google Scholar 

  21. Ye, H. et al. Equation-free mechanistic ecosystem forecasting using empirical dynamic modeling. Proc. Natl Acad. Sci. USA 112, E1569–E1576 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Perretti, C. T., Munch, S. B. & Sugihara, G. Model-free forecasting outperforms the correct mechanistic model for simulated and experimental data. Proc. Natl Acad. Sci. USA 110, 5253–5257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Greenman, J. V. & Benton, T. G. The amplification of environmental noise in population models: causes and consequences. Am. Nat. 161, 225–239 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effects of fishing. Proc. Natl Acad. Sci. USA 108, 7075–7080 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rogers, L. A., Moore, Z., Daigle, A., Luijckx, P. & Krkošek, M. Experimental evidence of size-selective harvest and environmental stochasticity effects on population demography, fluctuations and non-linearity. Ecol. Lett. 26, 586–596 (2023).

    Article  PubMed  Google Scholar 

  26. Ricard, D., Minto, C., Jensen, O. P. & Baum, J. K. Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish Fish. 13, 380–398 (2012).

    Article  Google Scholar 

  27. Thorson, J. T. et al. Identifying direct and indirect associations among traits by merging phylogenetic comparative methods and structural equation models. Methods Ecol. Evol. 14, 1259–1275 (2023).

    Article  Google Scholar 

  28. Huang, B. et al. Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1. J. Clim. 34, 2923–2939 (2021).

  29. Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Munch, S. B., Rogers, T. L. & Sugihara, G. Recent developments in empirical dynamic modelling. Methods Ecol. Evol. 14, 732–745 (2023).

    Article  Google Scholar 

  31. Sugihara, G. & May, R. M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990).

    Article  CAS  PubMed  Google Scholar 

  32. Sugihara, G. Nonlinear forecasting for the classification of natural time series. Philos. Trans. A 348, 477–495 (1994).

    Google Scholar 

  33. Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Drinkwater, K. F. & Kristiansen, T. A synthesis of the ecosystem responses to the late 20th century cold period in the northern North Atlantic. ICES J. Mar. Sci. 75, 2325–2341 (2018).

    Article  Google Scholar 

  35. Frank, K. T., Petrie, B., Leggett, W. C. & Boyce, D. G. Towards a more balanced assessment of the dynamics of North Atlantic ecosystems—a comment on Drinkwater and Kristiansen (2018). ICES J. Mar. Sci. 76, 2489–2494 (2019).

    Article  Google Scholar 

  36. McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).

    Article  CAS  Google Scholar 

  37. Frank, K. T., Petrie, B., Fisher, J. A. D. & Leggett, W. C. Transient dynamics of an altered large marine ecosystem. Nature 477, 86–89 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. Morris, W. F. et al. Longevity can buffer plant and animal populations against changing climatic variability. Ecology 89, 19–25 (2008).

    Article  PubMed  Google Scholar 

  39. Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).

    Article  CAS  PubMed  Google Scholar 

  40. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  41. Roy, K., Jablonski, D., Valentine, J. W. & Rosenberg, G. Marine latitudinal diversity gradients: tests of causal hypotheses. Proc. Natl Acad. Sci. USA 95, 3699–3702 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Walters, C. J. & Martell, S. J. D. Fisheries Ecology and Management (Princeton Univ. Press, 2004).

  43. Myers, R. A. & Cadigan, N. G. Density-dependent juvenile mortality in marine demersal fish. Can. J. Fish. Aquat. Sci. 50, 1576–1590 (1993).

    Article  Google Scholar 

  44. Hsieh, C., Anderson, C. & Sugihara, G. Extending nonlinear analysis to short ecological time series. Am. Nat. 171, 71–80 (2008).

    Article  PubMed  Google Scholar 

  45. Liu, J. et al. Complexity of coupled human and natural systems. Science 317, 1513–1516 (2007).

    Article  CAS  PubMed  Google Scholar 

  46. Fogarty, M. J., Gamble, R. & Perretti, C. T. Dynamic complexity in exploited marine ecosystems. Front. Ecol. Evol. 4, 68 (2016).

    Article  Google Scholar 

  47. Volkoff, H. & Rønnestad, I. Effects of temperature on feeding and digestive processes in fish. Temperature 7, 307–320 (2020).

    Article  Google Scholar 

  48. Himes-Cornell, A. & Kasperski, S. Assessing climate change vulnerability in Alaska’s fishing communities. Fish. Res. 162, 1–11 (2015).

    Article  Google Scholar 

  49. Munday, P. L., Jones, G. P., Pratchett, M. S. & Williams, A. J. Climate change and the future for coral reef fishes. Fish Fish. 9, 261–285 (2008).

    Article  Google Scholar 

  50. Cinner, J. E. et al. Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Glob. Environ. Change 22, 12–20 (2012).

    Article  Google Scholar 

  51. Frank, K. T., Petrie, B., Leggett, W. C. & Boyce, D. G. Large scale, synchronous variability of marine fish populations driven by commercial exploitation. Proc. Natl Acad. Sci. USA 113, 8248–8253 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Schweigert, J. F., Boldt, J. L., Flostrand, L. & Cleary, J. S. A review of factors limiting recovery of Pacific herring stocks in Canada. ICES J. Mar. Sci. 67, 1903–1913 (2010).

    Article  Google Scholar 

  53. Giron-Nava, A. et al. Environmental variability and fishing effects on the Pacific sardine fisheries in the Gulf of California. Can. J. Fish. Aquat. Sci. 78, 623–630 (2021).

    Article  Google Scholar 

  54. Tsai, C.-H., Munch, S. B., Masi, M. D. & Stevens, M. H. Empirical dynamic modeling for sustainable benchmarks of short-lived species. ICES J. Mar. Sci. 81, 1209–1220 (2024).

    Article  Google Scholar 

  55. Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).

    Article  Google Scholar 

  56. Andersen, K. H., Jacobsen, N. S., Jansen, T. & Beyer, J. E. When in life does density dependence occur in fish populations?. Fish Fish. 18, 656–667 (2017).

    Article  Google Scholar 

  57. Becker, Ri., Wilks, A., Brownrigg, R., Minka, T. & Deckmyn, A. maps: draw geographical maps. R package version 3.4.3 https://github.com/adeckmyn/maps (2025).

  58. Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 22, 57–75 (2014).

    Article  Google Scholar 

  59. Thorson, J. T. Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data-integrated life-history model. Fish Fish. 21, 237–251 (2020).

    Article  Google Scholar 

  60. Park, J. et al. rEDM: applications of empirical dynamic modeling from time series. R package version 1.15.4 https://github.com/SugiharaLab/rEDM (2025).

  61. Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).

    Article  CAS  PubMed  Google Scholar 

  62. Ye, H., Deyle, E. R., Gilarranz, L. J. & Sugihara, G. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep. 5, 14750 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Article  Google Scholar 

  64. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).

  65. Morey, R. BayesFactor: computation of Bayes factors for common designs. R package version 0.9.12-4.7 https://github.com/richarddmorey/BayesFactor (2023).

  66. Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article  Google Scholar 

  67. Hechler, R. & Krkosek, M. Data and code for: temperature variation and life history mediate nonlinearity in fluctuations of marine fish populations worldwide. Zenodo https://doi.org/10.5281/zenodo.17582260 (2025).

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Acknowledgements

We thank the many individuals and organizations that contributed to the databases that made our study possible. This study was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Vanier Canada Graduate Scholarship, Ontario Graduate Scholarship, American Society of Naturalists Student Research Award and Connaught PhDs for Public Impact Fellowship to R.M.H. and an NSERC Discovery Grant (RGPIN-2024-05054) and Canada Research Chair (CRC-2020-00234) to M.K.

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada

    Robert M. Hechler & Martin Krkosek

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  1. Robert M. Hechler
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Contributions

Conceptualization: R.M.H. and M.K. Methodology: R.M.H. and M.K. Formal analysis: R.M.H. Visualization: R.M.H. Supervision: M.K. Writing—original draft: R.M.H. Writing—review and editing: R.M.H. and M.K.

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Correspondence to Robert M. Hechler.

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Nature Ecology & Evolution thanks George Sugihara 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 The number of analyzed populations per species.

Histograms showing the number of populations per species used in our analyses, of A) recruits, B) spawners and C) total catch.

Extended Data Fig. 2 Principal component analysis of fish life history traits.

Principal component analyses of species life history traits: intrinsic growth rate (r), trophic level, log-max length, log-max age, log-age at maturity, log-fecundity and log-growth coefficient. We used PC1 and PC2 as predictors for our models.

Extended Data Fig. 3 Sensitivity analysis 1.

Sensitivity analysis 1 using the 2% threshold. We show the median point estimates and 95% credible intervals of our predictors for 100 hurdle gamma models.

Extended Data Fig. 4 Sensitivity analysis 2.

Sensitivity analysis 2 using the 3.5% threshold. We show the median point estimates and 95% credible intervals of our predictors for 100 hurdle gamma models.

Extended Data Fig. 5 Sensitivity analysis 3.

Sensitivity analysis 3 using the value of θ for all stocks that produced the second greatest value of ρ. We present the posterior distributions along with a point estimate for the posterior median and the 66% (thick black line) and 95% (thin black line) credible intervals.

Extended Data Fig. 6 Sensitivity analysis 4.

Sensitivity analysis 4 using temperature correlates calculated from an alternative SST database, the COBE-SST 2 database. We present the posterior distributions along with a point estimate for the posterior median and the 66% (thick black line) and 95% (thin black line) credible intervals.

Extended Data Fig. 7 Sensitivity analysis 5.

Sensitivity analysis 5 showing that the main results held after correcting for phylogenetic non-independence using a phylogenetic correlation matrix random effect. We present the posterior distributions along with a point estimate for the posterior median and the 66% (thick black line) and 95% (thin black line) credible intervals. A) θ was the response variable, and B) Δρ was the response variable.

Extended Data Fig. 8 Causal forcing of temperature on total catch dynamics.

Bayesian Bernoulli model shows the effects of SST, life history and habitat correlates on the probability that a population is causally forced by SST as identified by convergent cross mapping (CCM). We present the posterior distributions along with a point estimate for the posterior median and the 66% (thick black line) and 95% (thin black line) credible intervals. All fixed effects were scaled to a mean of 0 and standard deviation of 1. Of the 311 total catch time series, 213 were causally forced by temperature as identified by CCM. See Fig. 4 for recruitment and spawner biomass analysis.

Supplementary information

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Supplementary Tables 1 and 2, Supplementary Figs. 1–5, Sensitivity Analysis Methods and Supporting Text.

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Hechler, R.M., Krkosek, M. Temperature variation and life history mediate nonlinearity in fluctuations of marine fish populations worldwide. Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-025-02968-1

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