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Predator mass mortality events restructure food webs through trophic decoupling

Nature volume 626, pages 335–340 (2024)Cite this article

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Abstract

Predators have a key role in structuring ecosystems1,2,3,4. However, predator loss is accelerating globally4,5,6, and predator mass-mortality events7 (MMEs)—rapid large-scale die-offs—are now emblematic of the Anthropocene epoch6. Owing to their rare and unpredictable nature7, we lack an understanding of how MMEs immediately impact ecosystems. Past predator-removal studies2,3 may be insufficient to understand the ecological consequences of MMEs because, in nature, dead predators decompose in situ and generate a resource pulse8, which could alter ensuing ecosystem dynamics by temporarily enhancing productivity. Here we experimentally induce MMEs in tritrophic, freshwater lake food webs and report ecological dynamics that are distinct from predator losses2,3 or resource pulses9 alone, but that can be predicted from theory8. MMEs led to the proliferation of diverse consumer and producer communities resulting from weakened top-down predator control1,2,3 and stronger bottom-up effects through predator decomposition8. In contrast to predator removals alone, enhanced primary production after MMEs dampened the consumer community response. As a consequence, MMEs generated biomass dynamics that were most similar to those of undisturbed systems, indicating that they may be cryptic disturbances in nature. These biomass dynamics led to trophic decoupling, whereby the indirect beneficial effects of predators on primary producers are lost and later materialize as direct bottom-up effects that stimulate primary production amid intensified herbivory. These results reveal ecological signatures of MMEs and demonstrate the feasibility of forecasting novel ecological dynamics arising with intensifying global change.

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Fig. 1: Food-web biomass responses to predator removals, resource pulses and MMEs.
Fig. 2: Community-wide biomass, density and functional trait responses to predator removals, resource pulses and MMEs.
Fig. 3: Distinct zooplankton and microalgal community biomass trajectories after predator removals, resource pulses and MMEs.

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Data availability

All data for analyses are available at a permanent Zenodo repository (https://doi.org/10.5281/zenodo.10070514). Source data are provided with this paper.

Code availability

All code for analyses is available at a permanent Zenodo repository (https://doi.org/10.5281/zenodo.10070514).

References

  1. Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94, 421–425 (1960).

    Article  Google Scholar 

  2. Paine, R. T. Food web complexity and species diversity. Am. Nat. 100, 65–75 (1966).

    Article  Google Scholar 

  3. Paine, R. T. Food webs: linkage, interaction strength and community infrastructure. J. Anim. Ecol. 49, 666–685 (1980).

    Article  ADS  Google Scholar 

  4. Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Myers, R. A. & Worm, B. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  7. Fey, S. B. et al. Recent shifts in the occurrence, cause, and magnitude of animal mass mortality events. Proc. Natl Acad. Sci. USA 112, 1083–1088 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Fey, S. B., Gibert, J. P. & Siepielski, A. M. The consequences of mass mortality events for the structure and dynamics of biological communities. Oikos 128, 1679–1690 (2019).

    Article  ADS  Google Scholar 

  9. Polis, G. A., Anderson, W. B. & Holt, R. D. Toward an integration of landscape and food web ecology: the dynamics of spatially subsidized food webs. Annu. Rev. Ecol. Syst. 28, 289–316 (1997).

    Article  Google Scholar 

  10. Hamilton, S. L. et al. Disease-driven mass mortality event leads to widespread extirpation and variable recovery potential of a marine predator across the eastern Pacific. Proc. R. Soc. B 288, 20211195 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tye, S. P. et al. Climate warming amplifies the frequency of fish mass mortality events across north temperate lakes. Limnol. Oceanogr. Lett. 7, 510–519 (2022).

    Article  Google Scholar 

  12. Threlkeld, S. T. Planktivory and planktivore biomass effects on zooplankton, phytoplankton, and the trophic cascade: fish effects on plankton. Limnol. Oceanogr. 33, 1362–1375 (1988).

    Article  ADS  Google Scholar 

  13. Vanni, M. J. & Findlay, D. L. Trophic cascades and phytoplankton community structure. Ecology 71, 921–937 (1990).

    Article  Google Scholar 

  14. Leopold, A. A Sand County Almanac 129–133 (Random House, 1949).

  15. Schindler, D. W. Eutrophication and recovery in experimental lakes: implications for lake management. Science 184, 897–899 (1974).

    Article  ADS  CAS  PubMed  Google Scholar 

  16. Vitousek, P. M. Beyond global warming: ecology and global change. Ecology 75, 1861–1876 (1994).

    Article  Google Scholar 

  17. Bergquist, A. M. & Carpenter, S. R. Limnetic herbivory: effects on phytoplankton populations and primary production. Ecology 67, 1351–1360 (1986).

    Article  Google Scholar 

  18. Hansen, P. J., Bjørnsen, P. K. & Hansen, B. W. Zooplankton grazing and growth: Scaling within the 2–2,000-μm body size range. Limnol. Oceanogr. 42, 687–704 (1997).

    Article  ADS  Google Scholar 

  19. Porter, K. G. Selective grazing and differential digestion of algae by zooplankton. Nature 244, 179–180 (1973).

    Article  ADS  Google Scholar 

  20. Su, H. et al. Determinants of trophic cascade strength in freshwater ecosystems: a global analysis. Ecology 102, e03370 (2021).

    Article  PubMed  Google Scholar 

  21. Boros, G., Takács, P. & Vanni, M. J. The fate of phosphorus in decomposing fish carcasses: a mesocosm experiment. Freshw. Biol. 60, 479–489 (2015).

    Article  CAS  Google Scholar 

  22. Brooks, J. L. & Dodson, S. I. Predation, body size, and composition of plankton: the effect of a marine planktivore on lake plankton illustrates theory of size, competition, and predation. Science 150, 28–35 (1965).

    Article  ADS  CAS  PubMed  Google Scholar 

  23. Galbraith, M. G. Size-selective predation on daphnia by rainbow trout and yellow perch. Trans. Am. Fish. Soc. 96, 1–10 (1967).

    Article  Google Scholar 

  24. DeMott, W. R. & Kerfoot, W. C. Competition among cladocerans: nature of the interaction between bosmina and daphnia. Ecology 63, 1949 (1982).

    Article  Google Scholar 

  25. Gibert, J. P., Han, Z. Y., Wieczynski, D. J., Votzke, S. & Yammine, A. Feedbacks between size and density dependence determine rapid eco-phenotypic dynamics. Funct. Ecol. 6, 1668–1680 (2022).

    Article  Google Scholar 

  26. Borer, E. T. et al. What determines the strength of a trophic cascade? Ecology 86, 528–537 (2005).

    Article  Google Scholar 

  27. Vanni, M. J. et al. Effects on lower trophic levels of massive fish mortality. Nature 344, 333–335 (1990).

    Article  ADS  Google Scholar 

  28. Nagdali, S. S. & Gupta, P. K. Impact of mass mortality of a mosquito fish, Gambusia affinis on the ecology of a fresh water eutrophic lake (Lake Naini Tal, India). Hydrobiologia 468, 45–52 (2002).

    Article  Google Scholar 

  29. Vanni, M. J. Nutrient cycling by animals in freshwater ecosystems. Annu. Rev. Ecol. Syst. 33, 341–370 (2002).

    Article  Google Scholar 

  30. Van Donk, E., Ianora, A. & Vos, M. Induced defences in marine and freshwater phytoplankton: a review. Hydrobiologia 668, 3–19 (2011).

    Article  Google Scholar 

  31. Conley, D. J. et al. Controlling eutrophication: nitrogen and phosphorus. Science 323, 1014–1015 (2009).

    Article  CAS  PubMed  Google Scholar 

  32. De Cáceres, M. et al. Trajectory analysis in community ecology. Ecol. Monogr. 89, 1–20 (2019).

  33. Mougi, A. Coupling of green and brown food webs and ecosystem stability. Ecol. Evol. 10, 9192–9199 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442, 265–269 (2006).

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Fricke, E. C. et al. Collapse of terrestrial mammal food webs since the Late Pleistocene. Science 377, 1008–1011 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  36. Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).

    Article  ADS  Google Scholar 

  37. Ives, A. R. & Cardinale, B. J. Food-web interactions govern the resistance of communities after non-random extinctions. Nature 429, 174–177 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  38. MacArthur, R. Fluctuations of animal populations and a measure of community stability. Ecology 36, 533–536 (1955).

    Article  Google Scholar 

  39. McCann, K. S. The diversity–stability debate. Nature 405, 228–233 (2000).

    Article  CAS  PubMed  Google Scholar 

  40. Forbes, S. The lake as a microcosm. INHS Bull. 15, 537–550 (1925).

    Article  Google Scholar 

  41. Spivak, A. C., Vanni, M. J. & Mette, E. M. Moving on up: can results from simple aquatic mesocosm experiments be applied across broad spatial scales? Freshw. Biol. 56, 279–291 (2011).

    Article  Google Scholar 

  42. Dzialowski, A. R. et al. Are the abiotic and biotic characteristics of aquatic mesocosms representative of in situ conditions? J. Limnol. 73, 603–612 (2014).

    Article  Google Scholar 

  43. Bottrell, H. H. et al. A review of some problems in zooplankton production studies. Nor. J. Zool. 24, 419–456 (1976).

    Google Scholar 

  44. Davis, C. S. & Wiebe, P. H. Macrozooplankton biomass in a warm-core Gulf Stream ring: time series changes in size structure, taxonomic composition, and vertical distribution. J. Geophys. Res. 90, 8871–8884 (1985).

    Article  ADS  Google Scholar 

  45. Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).

    Article  ADS  CAS  Google Scholar 

  46. Verity, P. G. et al. Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol. Oceanogr. 37, 1434–1446 (1992).

    Article  ADS  CAS  Google Scholar 

  47. Vanni, M. J. Competition in zooplankton communities: suppression of small species by Daphnia pulex 1: competition among zooplankton. Limnol. Oceanogr. 31, 1039–1056 (1986).

    Article  ADS  CAS  Google Scholar 

  48. Hall, D. J., Cooper, W. E. & Werner, E. E. An experimental approach to the production dynamics and structure of freshwater animal communities. Limnol. Oceanogr. 15, 839–928 (1970).

    Article  ADS  Google Scholar 

  49. Bartell, S. M. Influence of prey abundance on size-selective predation by bluegills. Trans. Am. Fish. Soc. 111, 453–461 (1982).

    Article  Google Scholar 

  50. Neiffer, D. L. & Stamper, M. A. Fish sedation, anesthesia, analgesia, and euthanasia: considerations, methods, and types of drugs. ILAR J. 50, 343–360 (2009).

    Article  CAS  PubMed  Google Scholar 

  51. Barnett, A. J., Finlay, K. & Beisner, B. E. Functional diversity of crustacean zooplankton communities: towards a trait-based classification. Freshw. Biol. 52, 796–813 (2007).

    Article  Google Scholar 

  52. Cupertino, A., Gücker, B., Von Rückert, G. & Figueredo, C. C. Phytoplankton assemblage composition as an environmental indicator in routine lentic monitoring: taxonomic versus functional groups. Ecol. Indic. 101, 522–532 (2019).

    Article  CAS  Google Scholar 

  53. Arar, E. J. In Vitro Determination of Chlorophyll a and Pheophytin a in Marine and Freshwater Algae by Fluorescence.(Environmental Protection Agency, 1997).

  54. Standard Operating Procedure For In Vitro Determination of Chlorophyll-a in Freshwater Phytoplankton by Fluorescence (Environmental Protection Agency, 2013).

  55. Knefelkamp, B., Carstens, K. & Wiltshire, K. H. Comparison of different filter types on chlorophyll-a retention and nutrient measurements. J. Exp. Mar. Biol. Ecol. 345, 61–70 (2007).

    Article  CAS  Google Scholar 

  56. Arar, E. J. Evaluation of a New Fluorometric Technique That Uses Highly Selective Interference Filters For Measuring Chlorophyll a in the Presence of Chlorophyll b and Pheopigments (Environmental Protection Agency, 1994).

  57. Welschmeyer, N. A. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and pheopigments. Limnol. Oceanogr. 39, 1985–1992 (1994).

    Article  ADS  CAS  Google Scholar 

  58. Hrycik, A. R., Shambaugh, A. & Stockwell, J. D. Comparison of FlowCAM and microscope biovolume measurements for a diverse freshwater phytoplankton community. J. Plankton Res. 41, 849–864 (2019).

    Article  Google Scholar 

  59. Breier, C. F. & Buskey, E. J. Effects of the red tide dinoflagellate, Karenia brevis, on grazing and fecundity in the copepod Acartia tonsa. J. Plankton Res. 29, 115–126 (2007).

    Article  Google Scholar 

  60. Mack, H. R., Conroy, J. D., Blocksom, K. A., Stein, R. A. & Ludsin, S. A. A comparative analysis of zooplankton field collection and sample enumeration methods: zooplankton sampling and counting methods. Limnol. Oceanogr. Methods 10, 41–53 (2012).

    Article  Google Scholar 

  61. Bogdan, K. G. & Gilbert, J. J. Body size and food size in freshwater zooplankton. Proc. Natl Acad. Sci. USA 81, 6427–6431 (1984).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 

  63. Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).

  64. Savage, V. M., Gillooly, J. F., Brown, J. H., West, G. B. & Charnov, E. L. Effects of body size and temperature on population growth. Am. Nat. 163, 429–441 (2004).

    Article  PubMed  Google Scholar 

  65. De Gooijer, J. G. & Hyndman, R. J. 25 years of time series forecasting. Int. J. Forecast. 22, 443–473 (2006).

    Article  Google Scholar 

  66. Robert, H. S. & Stoffer, D. S. Time Series Analysis and Its Application (Springer, 2017).

  67. De Livera, A. M., Hyndman, R. J. & Snyder, R. D. Forecasting time series with complex seasonal patterns using exponential smoothing. J. Am. Stat. Assoc. 106, 1513–1527 (2011).

    Article  MathSciNet  Google Scholar 

  68. Mann, M. E. Smoothing of climate time series revisited. Geophys. Res. Lett. 35, L16708 (2008).

    Article  ADS  Google Scholar 

  69. Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).

    Article  MathSciNet  Google Scholar 

  70. Pinherio, J., Bates, D. & R Core Team. nlme: linear and nonlinear mixed effects models (2022).

  71. Wood, S. N. Thin-plate regression spine. J. R. Stat. Soc. B 65, 95–114 (2003).

    Article  Google Scholar 

  72. Sturbois, A. et al. Extending community trajectory analysis: new metrics and representation. Ecol. Modell. 440, 109400 (2021).

    Article  Google Scholar 

  73. Dexter, E., Rollwagen-Bollens, G. & Bollens, S. M. The trouble with stress: a flexible method for the evaluation of nonmetric multidimensional scaling. Limnol. Oceanogr. Methods 16, 434–443 (2018).

    Article  Google Scholar 

  74. Tilman, D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80, 1455–1474 (1999).

    Google Scholar 

  75. Domínguez-García, V., Dakos, V. & Kéfi, S. Unveiling dimensions of stability in complex ecological networks. Proc. Natl Acad. Sci. USA 116, 25714–25720 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank the staff at the Arkansas Game and Fish Commission and University of Arkansas Facilities and Agricultural Facilities, A. J. Alverson, J. D. Willson, E. Ruck, T. Nakov, J. Boyko, K. Smith, A. Ingram, W. Boys and M. Gómez-Llano for helping with the study; M. Cordellier, M. Crook, M. Dahirel, H. N. Eyster, C. Gross, M. Kodis and S. A. Muñoz-Gómez for contributing to PhyloPic. S.P.T. was supported in part by the NSF (GRFP 1842401). S.B.F. was supported by NSF DEB 1856415 and 2236526. J.P.G. was supported by DOE BER DE-SC0020362, NSF DEB 2224819 and Simons Foundation Early Career Fellowship in Microbial Ecology and Evolution LS-ECIAMEE-00001588. A.M.S. was supported by NSF DEB 1748945 and 2306183.

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Authors and Affiliations

  1. Department of Biological Sciences, University of Arkansas, Fayetteville, AR, USA

    Simon P. Tye & Adam M. Siepielski

  2. Department of Biology, Reed College, Portland, OR, USA

    Samuel B. Fey

  3. Department of Biology, Duke University, Durham, NC, USA

    Jean P. Gibert

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  1. Simon P. Tye
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  2. Samuel B. Fey
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Contributions

S.P.T., S.B.F., J.P.G. and A.M.S. developed the idea and experimental approach. A.M.S. secured funding and supervised the project. S.P.T. conducted the experiment, compiled data and conducted analyses. S.P.T., S.B.F., J.P.G. and A.M.S. wrote the manuscript and contributed to revisions.

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Correspondence to Simon P. Tye or Adam M. Siepielski.

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Extended data figures and tables

Extended Data Fig. 1 Time series of mean zooplankton and microalgae density following ecological perturbations.

Mean density (ln[individuals/L]) of zooplankton (A) and microalgae (B) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs (t11.42 = 7.51, p < 0.01), predator removals (t11.42 = 7.35, p < 0.01), and, to a lesser extent, resource pulses (t11.42 = 3.73, p < 0.01) increased mean zooplankton density. MMEs (t13.36 = −7.95, p < 0.01), predator removals (t13.36 = −5.28, p < 0.01), and, to a lesser extent, resource pulses (t13.36 = −2.43, p = 0.02) decreased mean microalgae density. Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Data were analysed from 355 biologically independent zooplankton samples (A) and microalgae samples (B) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-B).

Extended Data Fig. 2 Time series of mean zooplankton body size and microalgae biovolume following ecological perturbations.

Mean zooplankton body size (mm, A) and microalgae biovolume (ln[μm2], B) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs (t8 = 8.42, p < 0.01), predator removals (t8 = 6.29, p < 0.01), and resource pulses (t8 = 4.99, p < 0.01) increased mean zooplankton body size. MMEs (t15.06 = 2.58, p = 0.02) and predator removals (t15.06 = 2.48, p = 0.02), but not resource pulses (t15.06 = 1.32, p = 0.19), increased mean microalgae biovolume. Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Data were analysed from 355 biologically independent zooplankton samples (A) and microalgae samples (B) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-B).

Extended Data Fig. 3 Time series of mean biomass across five major zooplankton families following ecological perturbations.

Zooplankton biomass (ln[μg/L]) of Bosminidae (A), Brachionidae (B), Cyclopoida (C), Cyprididae (D), and Daphniidae (E) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs increased mean biomass of Bosminidae (t8.36 = 5.74, p < 0.01; A), Brachionidae (t9.78 = 2.31, p = 0.02; B), Cyclopoida (t9.59 = 8.33, p < 0.01; C), Cyprididae (t15 = 2.98, p < 0.01; D), and Daphniidae (t9.34 = 9.31, p < 0.01; E). Predator removals increased mean biomass of Bosminidae (t8.36 = 2.70, p = 0.008; A), Cyclopoida (t9.59 = 10.49, p < 0.01; C) and Daphniidae (t9.34 = 8.84, p < 0.01; E). Resource pulses increased mean biomass of Cyclopoida (t9.59 = 8.42, p < 0.01; C), Cyprididae (t15 = 2.30, p = 0.01; D), and Daphniidae (t9.34 = 5.23, p < 0.01; E). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major zooplankton families as in main figures. Data were analysed from 355 biologically independent zooplankton samples (A-E) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-E).

Extended Data Fig. 4 Time series of mean biomass across five major microalgae phyla following ecological perturbations.

Microalgae biomass (ln[μg/L]) of Charophyta (A), Chlorophyta (B), Cyanobacteria (C), Dinoflagellata (D), and Euglenozoa (E) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs decreased mean biomass of Charophyta (t12.81 = −9.93, p < 0.01; A) and Dinoflagellata (t22.1 = −5.04, p < 0.01; D), and had relatively low mean Cyanobacteria biomass (t22.1 = −1.85, p = 0.07; C). Predator removals decreased mean biomass of Charophyta (t12.81 = −5.69, p < 0.01; A) and Dinoflagellata (t22.1 = −2.47, p = 0.01; D). Resource pulses increased mean biomass of Chlorophyta (t10.01 = 3.19, p = 0.01; B) and Cyanobacteria (t22.1 = 2.05, p = 0.04; C), and decreased mean biomass of Charophyta (t12.81 = −6.07, p < 0.01; A) and Dinoflagellata (t22.1 = −4.89, p < 0.01; D). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major microalgae phyla as in main figures. Data were analysed from 355 biologically independent zooplankton samples (A-E) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-E).

Extended Data Fig. 5 Time series of mean density across five major zooplankton families following ecological perturbations.

Zooplankton density (ln[individuals/L]) of Bosminidae (A), Brachionidae (B), Cyclopoida (C), Cyprididae (D), and Daphniidae (E) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs increased mean density of all major zooplankton families including Bosminidae (t10.4 = 8.05, p < 0.01; A), Brachionidae (t11.13 = 3.00, p < 0.01; B), Cyclopoida (t9.53 = 5.70, p < 0.01; C), Cyprididae (t7.44 = 2.09, p = 0.04; D), and Daphniidae (t9.4 = 4.38, p < 0.01; E). Predator removals increased mean density of Bosminidae (t10.4 = 8.36, p < 0.01; A), Brachionidae (t11.13 = 4.12, p < 0.01; B), Cyclopoida (t9.53 = 8.34, p < 0.01; C), and Daphniidae (t9.4 = 5.95, p < 0.01; E) as well as reduced mean Cyprididae density (t7.44 = −1.98, p = 0.05; D). Resource pulses increased mean density of Brachionidae (t11.13 = 2.99, p < 0.01; B) and Cyclopoida (t9.53 = 3.13, p < 0.01; C). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major zooplankton families as in main figures. Data were analysed from 355 biologically independent zooplankton samples (A-E) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-E).

Extended Data Fig. 6 Time series of mean density across five major microalgae phyla following ecological perturbations.

Microalgae density (ln([ndividuals/L]) of Charophyta (A), Chlorophyta (B), Cyanobacteria (C), Dinoflagellata (D), and Euglenozoa (E) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs decreased mean density of Charophyta (t12.42 = −9.83, p < 0.01; A) and Dinoflagellata (t9.87 = −5.18, p < 0.01; D), as well as had relatively low mean density of Cyanobacteria (t21.86 = −1.67, p = 0.10; C). Predator removals decreased density of Charophyta (t12.42 = −5.63, p < 0.01; A), Chlorophyta (t17.14 = −2.47, p = 0.02; B), and Dinoflagellata (t9.87 = −5.18, p < 0.01; D). Resource pulses increased mean density of Chlorophyta (t17.14 = 2.86, p = 0.01; B) and reduced mean density of Charophyta (t12.42 = −5.98, p < 0.01; A) and Dinoflagellata (t9.87 = −5.11, p < 0.01; D), as well as had relatively high mean density of Cyanobacteria (t21.86 = 1.93, p = 0.06; C). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major microalgae phyla as in main figures. Data were analysed from 355 biologically independent zooplankton samples (A-E) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), respectively, from one experiment. General additive mixed model (GAMM, A-E).

Extended Data Fig. 7 Time series of mean body size across five major zooplankton families following ecological perturbations.

Zooplankton body size (mm) of Bosminidae (A), Brachionidae (B), Cyclopoida (C), Cyprididae (D), and Daphniidae (E) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs increased mean body size of Bosminidae (t5 = 2.98, p < 0.01; A), Cyclopoida (t12.93 = 7.57, p < 0.01; C), and Daphniidae (t5 = 6.71, p < 0.01; E). Predator removals increased mean body size of Cyclopoida (t12.93 = 6.98, p < 0.01; C) and Daphniidae (t5 = 5.09, p < 0.001; E), as well as decreased mean body size of Brachionidae (t5 = −3.17, p < 0.01; B). Resource pulses increased mean body size of Cyclopoida (t12.93 = 2.68, p = 0.01; C), Cyprididae (t9.74 = 2.16, p = 0.03; D), and Daphniidae (t5 = 3.88, p < 0.01; E), as well as decreased mean body size of Brachionidae (t5 = −2.14, p = 0.03; B). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major zooplankton families as in main figures. Data were analysed from 355 biologically independent zooplankton samples (A-E) for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 969, respectively, from one experiment. General additive mixed model (GAMM, A-E).

Extended Data Fig. 8 Time series of mean biovolume across two major microalgae phyla following ecological perturbations.

Mean microalgae biovolume (ln[μg2]) of Charophyta (A) and Chlorophyta (B) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and +/− 1 SE for each sampling period and treatment, with points jittered to reduce overlap within sample periods. Solid lines and shaded regions indicate model predictions and 95% confidence intervals, respectively. Relative to the control, MMEs increased mean biovolume of Charophyta (t8.83 = 2.47, p = 0.01; A) and Chlorophyta (t20.96 = 2.93, p < 0.01; B). Predator removals increased mean biovolume of Chlorophyta (t20.96 = 4.32, p < 0.01; A). Dashed lines indicate when perturbations were induced (i.e., live fish and/or fish carrion added and/or removed). Taxa silhouettes and their colours correspond with major microalgae phyla as in main figures. Data were analysed from 240 biologically independent samples of Charophyta (A) for a control, (n = 80), predator removal (n = 54), resource pulse (n = 54), and MME (n = 52), as well as 283 biologically independent samples of Chlorophyta (B) for a control, (n = 79), predator removal (n = 59), resource pulse (n = 70), and MME (n = 75), from one experiment. General additive mixed model (GAMM, A-B).

Extended Data Fig. 9 Raw biomass estimates during sample periods before ecological perturbations were induced (i.e., the first three sample periods).

Chlorophyll-a (ln[μg/L], A), zooplankton biomass (ln[μg/L], B), and microalgae biomass (ln[μg/L], C) in mesocosms that would receive treatments to become predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Large points and lines indicate mean values and 95% CIs, with smaller jittered points (to reduce overlap) indicating individual samples. There were no effects of treatment on average chlorophyll-a (F1,3 = 0.11, p = 0.65; A), zooplankton biomass (F1,3 = 0.03, p = 0.99; B), or microalgae biomass (F1,3 = 0.94, p = 0.45; C) during the first three sample periods of the experiment. Data were analysed from 54 biologically independent samples for a control, (n = 15), predator removal (n = 12), resource pulse (n = 12), and MME (n = 15). Two-way, two-sided ANOVA (A-B).

Extended Data Fig. 10 Raw (non-smoothed) and smoothed biomass estimates following ecological perturbations.

Chlorophyll-a (ln[μg/L], A-B), zooplankton biomass (ln[μg/L], C-D), and microalgae biomass (ln[μg/L], E-F) following predator mass mortality events (MMEs, light green), predator removals (light blue), and resource pulses (dark green), as well as the undisturbed system (control, dark blue). Points and lines indicate mean values and 95% CIs, with points jittered to reduce overlap. Raw data (A, C, E) were similar to smoothed data (B, D, F, as in Fig. 1f–h). Specifically, relative to the experimental control, predator removals decreased average chlorophyll-a (F1,14 = 5.50, p = 0.03; A), resource pulses increased average chlorophyll-a (F1,14 = 151.48, p < 0.01), and there was no significant interaction between predator removals and resource pulses (i.e., MMEs) on chlorophyll-a (F1,14 = 0.26, p = 0.62). Additionally, predator removals increased zooplankton biomass (F1,14 = 5.03, p = 0.04; C), but there was no effect of resource pulses on zooplankton biomass (F1,14 = 0.56, p = 0.47) nor was there a significant interaction between predator removals and resource pulses on zooplankton biomass (F1,14 = 0.07, p = 0.80). Lastly, there was strong evidence that predator removals reduced microalgae biomass (F1,14 = 4.16, p = 0.06; E), but there was no effect of resource pulses on microalgae biomass (F1,14 = 0.64, p = 0.43) nor was there a significant interaction between predator removals and resource pulses on microalgae biomass (F1,14 = 0.01, p = 0.94). Results for the analysis of the smoothed data are shown in the main text. Raw data were analysed from 391 biologically independent samples for a control (n = 106), predator removal (n = 88), resource pulse (n = 88), and MME (n = 109). Smoothed data were analysed from 355 biologically independent samples for a control (n = 96), predator removal (n = 80), resource pulse (n = 80), and MME (n = 99), from one experiment. Two-way, two-sided ANOVA (A-B).

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Tye, S.P., Fey, S.B., Gibert, J.P. et al. Predator mass mortality events restructure food webs through trophic decoupling. Nature 626, 335–340 (2024). https://doi.org/10.1038/s41586-023-06931-7

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