Consumer-resource dynamics is an eco-evolutionary process in a natural plankton community


When traits affecting species interactions evolve rapidly, ecological dynamics can be altered while they occur. These eco-evolutionary dynamics have been documented repeatedly in laboratory and mesocosm experiments. We show here that they are also important for understanding community functioning in a natural ecosystem. Daphnia is a major planktonic consumer influencing seasonal plankton dynamics in many lakes. It is also sensitive to succession in its phytoplankton food, from edible algae in spring to relatively inedible cyanobacteria in summer. We show for Daphnia mendotae in Oneida Lake, New York, United States, that within-year ecological change in phytoplankton (from spring diatoms, cryptophytes and greens to summer cyanobacteria) resulted in consumers evolving increasing tolerance to cyanobacteria over time. This evolution fed back on ecological seasonal changes in population abundance of this major phytoplankton consumer. Oneida Lake is typical of mesotrophic lakes broadly, suggesting that eco-evolutionary consumer-resource dynamics is probably common.

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Fig. 1: Seasonal dynamics of Oneida Lake plankton in 2015 compared with stereotypical PEG model.
Fig. 2: Frequencies of D. mendotae clones in Oneida Lake 2015.
Fig. 3: Performance of Oneida Lake D. mendotae clones on spring and summer food.
Fig. 4: Evolution of Daphnia population mean JGR on the basis of clone-specific JGR values and seasonal phytoplankton composition.
Fig. 5: The projected effect on Daphnia population dynamics of its evolution in response to changing edibility of the phytoplankton food resource.

Data availability

The data underlying each of the figures and statistical analyses are freely available online at eCommons: Cornell University’s digital repository45 (

Code availability

R scripts for R-generated statistical analyses and figures are freely available online at eCommons: Cornell University’s digital repository45 (


  1. 1.

    Hendry, A. P. Eco-Evolutionary Dynamics (Princeton Univ. Press, 2017).

  2. 2.

    Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Hairston, N. G. Jr, Ellner, S. P., Geber, M. A., Yoshida, T. & Fox, J. A. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8, 1114–1127 (2005).

    Article  Google Scholar 

  4. 4.

    Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Jr. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Turcotte, M. M., Reznick, D. N. & Hare, J. D. Experimental assessment of the impact of rapid evolution on population dynamics. Evol. Ecol. Res. 13, 113–131 (2011).

    Google Scholar 

  6. 6.

    Hiltunen, T., Hairston, N. G. Jr., Hooker, G., Jones, L. E. & Ellner, S. P. A newly discovered role of evolution in previously published consumer-resource dynamics. Ecol. Lett. 17, 915–923 (2014).

    Article  PubMed  Google Scholar 

  7. 7.

    Bassar, R. D. et al. Local adaptation in Trinidadian guppies alters ecosystem processes. Proc. Natl Acad. Sci. USA 107, 3616–3621 (2010).

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Agrawal, A. A., Johnson, M. T. J., Hastings, A. & Maron, J. L. A field experiment demonstrating plant life-history evolution and its eco-evolutionary feedback to seed predator populations. Am. Nat. 181, S35–S45 (2013).

    Article  PubMed  Google Scholar 

  9. 9.

    Pantel, J. H., Duvivier, C. & De Meester, L. Rapid local adaptation mediates zooplankton community assembly in experimental mesocosms. Ecol. Lett. 18, 992–1000 (2015).

    Article  PubMed  Google Scholar 

  10. 10.

    Lampert, W. Daphnia: Development of a Model Organism in Ecology and Evolution (International Ecology Institute, 2011).

  11. 11.

    Miner, B. E., De Meester, L., Pfrender, M. E., Lampert, W. & Hairston, N. G.Jr. Linking genes to communities and ecosystems: Daphnia as an ecogenomic model. Proc. R. Soc. B 279, 1873–1882 (2012).

    Article  PubMed  Google Scholar 

  12. 12.

    Tessier, A. J. & Woodruff, P. Cryptic trophic cascade along a gradient of lake size. Ecology 83, 1263–1270 (2002).

    Article  Google Scholar 

  13. 13.

    Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton Univ. Press, 2002).

  14. 14.

    Rudstam, L. G., Lathrop, R. C. & Carpenter, S. R. The rise and fall of a dominant planktivore: direct and indirect effects on zooplankton. Ecology 74, 303–319 (1993).

    Article  Google Scholar 

  15. 15.

    Carpenter, S. R. et al. Regulation of lake primary productivity by food web structure. Ecology 68, 1863–1876 (1987).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Lampert, W. Laboratory studies on zooplankton–cyanobacteria interactions. N.Z. J. Mar. Freshwater Res. 21, 483–490 (1987).

    Article  Google Scholar 

  17. 17.

    Martin-Creuzburg, D., von Elert, E. & Hoffmann, K. H. Nutritional constraints at the cyanobacteria–Daphnia magna interface: the role of sterols. Limnol. Oceanogr. 53, 456–468 (2008).

    Article  Google Scholar 

  18. 18.

    Hairston, N. G.Jr. et al. Rapid evolution revealed by dormant eggs. Nature 401, 446 (1999).

    Article  Google Scholar 

  19. 19.

    Sarnelle, O. & Wilson, A. E. Local adaptation of Daphnia pulicaria to toxic cyanobacteria. Limnol. Oceanogr. 50, 1565–1570 (2005).

    Article  Google Scholar 

  20. 20.

    Chislock, M. F., Sarnelle, O., Olsen, B. K., Doster, E. & Wilson, A. E. Large effects of consumer offense on ecosystem structure and function. Ecology 94, 2375–2380 (2013).

    Article  PubMed  Google Scholar 

  21. 21.

    Cáceres, C. E. et al. in Oneida Lake: L ong-term Dynamics of a Managed Ecosystem and its Fishery (eds Rudstam, L. G, Mills, E. L., Jackson, J. R. & Stewart, D. L.) 201–226 (American Fisheries Society, 2016).

  22. 22.

    Cáceres, C. E. Interspecific variation in the abundance, production, and emergence of Daphnia diapausing eggs. Ecology 79, 1699–1710 (1998).

    Article  Google Scholar 

  23. 23.

    Lampert, W. & Trubetskova, I. Juvenile growth rate as a measure of fitness in Daphnia. Funct. Ecol. 10, 631–635 (1996).

    Article  Google Scholar 

  24. 24.

    Sommer, U., Gliwicz, M., Lampert, W. & Duncan, A. The PEG-model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol. 106, 433–471 (1986).

    Google Scholar 

  25. 25.

    Sommer, U. et al. Beyond the plankton ecology group (PEG) model: mechanisms driving plankton succession. Annu. Rev. Ecol. Evol. Syst. 43, 429–448 (2012).

    Article  Google Scholar 

  26. 26.

    Idrisi, N., Mills, E. L. & Rudstam, L. G. in Oneida Lake: Long-term Dynamics of a Managed Ecosystem and its Fishery (eds Rudstam, L. G, Mills, E. L., Jackson, J. R. & Stewart, D. L.) 139–159 (American Fisheries Society, 2016).

  27. 27.

    Hairston, N. G.Jr. Zooplankton egg banks as biotic reservoirs in changing environments. Limnol. Oceanogr. 41, 1087–1092 (1996).

    Article  Google Scholar 

  28. 28.

    Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994).

    Article  Google Scholar 

  29. 29.

    Ellner, S. P., Snyder, R. E. & Adler, P. B. How to quantify the temporal storage effect using simulations instead of math. Ecol. Lett. 19, 1333–1342 (2016).

    Article  PubMed  Google Scholar 

  30. 30.

    Ellner, S. P. & Hairston, N. G. Jr. Role of overlapping generations in maintaining genetic variation in a fluctuating environment. Am. Nat. 143, 403–417 (1994).

    Article  Google Scholar 

  31. 31.

    Hedrick, P. W. Genetic polymorphism in a temporally varying environment: effect of delayed germination or diapause. Heredity 75, 164–170 (1995).

    Article  Google Scholar 

  32. 32.

    Turelli, M., Schemske, D. W. & Bierzychudek, P. Stable two-allele polymorphisms maintained by fluctuating fitnesses and seed banks: protecting the blues in Linanthus parryae. Evolution 55, 1283–1298 (2001).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Hendry, A. P. & Kinnison, M. T. Perspective: the pace of modern life: measuring rates of contemporary evolution. Evolution 53, 1637–1653 (1999).

    Article  PubMed  Google Scholar 

  34. 34.

    Messer, P. W., Ellner, S. P. & Hairston, N. G.Jr. Can population genetics adapt to rapid evolution? Trends Genet. 32, 408–418 (2016).

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Rudstam, L. G., Mills, E. L., Jackson, J. R. & Stewart, D. J. in Oneida Lake: Long-term Dynamics of a Managed Ecosystem and its Fishery (eds Rudstam, L. G, Mills, E. L., Jackson, J. R. & Stewart, D. L.) 3–11 (American Fisheries Society, 2016).

  36. 36.

    Cuhel, R. L. &, Aguilar, C. in Oneida Lake: Long-term Dynamics of a Managed Ecosystem and its Fishery (eds Rudstam, L. G, Mills, E. L., Jackson, J. R. & Stewart, D. L.) 111–137 (American Fisheries Society, 2016).

  37. 37.

    Hairston, N. G.Jr. & Van Brunt, R. A. Diapause dynamics of two diaptomid copepod species in a large lake. Hydrobiologia 292, 209–218 (1994).

    Article  Google Scholar 

  38. 38.

    Hansen, A.-M. & Hairston, N. G. Jr. Food limitation in a wild cyclopoid copepod population: direct and indirect life history responses. Oecologia 115, 320–330 (1998).

    Article  PubMed  Google Scholar 

  39. 39.

    Hairston, N. G.Jr., Hansen, A.-M. & Schaffner, W. R. The effect of diapause emergence on the seasonal dynamics of a zooplankton assemblage. Freshw. Biol. 45, 133–145 (2000).

    Article  Google Scholar 

  40. 40.

    Brede, N. et al. Microsatellite markers for European Daphnia. Mol. Ecol. Notes 6, 536–539 (2006).

    CAS  Article  Google Scholar 

  41. 41.

    Hotto, A. M., Satchwell, M. F., Berry, D. L., Gobler, C. J. & Boyer, G. L. Spatial and temporal diversity of microcystins and microcystin-producing genotypes in Oneida Lake, NY. Harmful Algae 7, 671–681 (2008).

    CAS  Article  Google Scholar 

  42. 42.

    Montero-Pau, J., Gómez, A. & Munoz, J. Application of an inexpensive and high-throughput genomic DNA extraction method for the molecular ecology of zooplanktonic diapausing eggs. Limnol. Oceanogr. Methods 6, 218–222 (2008).

    CAS  Article  Google Scholar 

  43. 43.

    Meirmans, P. C. & Van Tienderen, P. H. GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4, 792–794 (2004).

    Article  Google Scholar 

  44. 44.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  45. 45.

    Schaffner, L. R. et al. Data from: Consumer-resource Dynamics is an Eco-Evolutionary Process in a Natural Plankton Community (Cornell Univ., 2019);

  46. 46.

    Pielou, E. C. An Introduction to Mathematical Ecology (John Wiley & Sons, 1969).

  47. 47.

    Greeson, P. E. Limnology of Oneida Lake with Emphasis on Factors Contributing to Algal Blooms (U.S. Geological Survey, 1971).

  48. 48.

    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

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Sample preparation for microsatellite DNA sequencing was carried out by E. Keller and S. Bogdanowicz advised on interpretation of genotype data. J. Geyer, D. Oden and A. Wong helped in the laboratory and field. W. Lampert contributed insights to the JGR measure. P. McIntyre, R. Wilkins, K. Sirianni, L. Zarri, E. Larson and members of the Hairston–Flecker laboratory groups provided helpful comments on the manuscript. The research was supported by US National Science Foundation grant no. DEB-1256719 to N.G.H., S.P.E. and B.E.M., KU Leuven Research Fund grant no. C16/2017/02 to L.D.M., a Research Foundation–Flanders travel grant and Agency for Innovation by Science and Technology PhD fellowship to L.G. and by Doris Duke Foundation internship funding to E.F. The CBFS and New York State Department of Environmental Conservation supported field sampling and processing.

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B.E.M., S.P.E. and N.G.H. conceived the study. L.R.S., B.E.M. and N.G.H. designed the field sampling; E.F., L.R.S. and L.G.R. and his laboratory carried it out. L.R.S. and N.G.H. designed the laboratory experiment and L.R.S. carried it out. P.S. and his laboratory did the microsatellite DNA sequencing. B.E.M. and L.R.S. analysed the microsatellite data to calculate genotype frequencies. N.G.H., L.R.S., L.G., L.D.M and S.P.E. analysed the experimental results. The first draft was written by L.R.S. and N.G.H. All authors contributed to revisions of the manuscript.

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Correspondence to Nelson G. Hairston Jr.

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Schaffner, L.R., Govaert, L., De Meester, L. et al. Consumer-resource dynamics is an eco-evolutionary process in a natural plankton community. Nat Ecol Evol 3, 1351–1358 (2019).

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