Ecological complexity buffers the impacts of future climate on marine consumers

  • Nature Climate Changevolume 8pages229233 (2018)
  • doi:10.1038/s41558-018-0086-0
  • Download Citation
Published online:


Ecological complexity represents a network of interacting components that either propagate or counter the effects of environmental change on individuals and communities1,2,3. Yet, our understanding of the ecological imprint of ocean acidification (elevated CO2) and climate change (elevated temperature) is largely based on reports of negative effects on single species in simplified laboratory systems4,5. By combining a large mesocosm experiment with a global meta-analysis, we reveal the capacity of consumers (fish and crustaceans) to resist the impacts of elevated CO2. While individual behaviours were impaired by elevated CO2, consumers could restore their performances in more complex environments that allowed for compensatory processes. Consequently, consumers maintained key traits such as foraging, habitat selection and predator avoidance despite elevated CO2 and sustained their populations. Our observed increase in risk-taking under elevated temperature, however, predicts greater vulnerability of consumers to predation. Yet, CO2 as a resource boosted the biomass of consumers through species interactions and may stabilize communities by countering the negative effects of elevated temperature. We conclude that compensatory dynamics inherent in the complexity of nature can buffer the impacts of future climate on species and their communities.

  • Subscribe to Nature Climate Change for full access:



Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Brown, J. H., Whitham, T. G., Ernest, S. K. M. & Gehring, C. A. Complex species interactions and the dynamics of ecological systems: Long-term experiments. Science 293, 643–650 (2001).

  2. 2.

    Leuzinger, S. et al. Do global change experiments overestimate impacts on terrestrial ecosystems?. Trends Ecol. Evol. 26, 236–241 (2011).

  3. 3.

    Rosenblatt, A. E. & Schmitz, O. J. Climate change, nutrition, and bottom-up and top-down food web processes. Trends Ecol. Evol. 31, 965–975 (2016).

  4. 4.

    Riebesell, U. & Gattuso, J. P. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 12–14 (2015).

  5. 5.

    Nagelkerken, I. & Munday, P. L. Animal behaviour shapes the ecological effects of ocean acidification and warming: moving from individual to community-level responses. Glob. Change Biol. 22, 974–989 (2016).

  6. 6.

    Schmidt, K. A., Dall, S. R. X. & van Gils, J. A. The ecology of information: an overview on the ecological significance of making informed decisions. Oikos 119, 304–316 (2010).

  7. 7.

    Sih, A. Understanding variation in behavioural responses to human-induced rapid environmental change: a conceptual overview. Anim. Behav. 85, 1077–1088 (2013).

  8. 8.

    Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).

  9. 9.

    McGann, J. P. Associative learning and sensory neuroplasticity: how does it happen and what is it good for? Learn. Mem. 22, 567–576 (2015).

  10. 10.

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

  11. 11.

    Heath, M. R., Speirs, D. C. & Steele, J. H. Understanding patterns and processes in models of trophic cascades. Ecol. Lett. 17, 101–114 (2014).

  12. 12.

    Connell, S. D. & Ghedini, G. Resisting regime-shifts: the stabilising effect of compensatory processes. Trends Ecol. Evol. 30, 513–515 (2015).

  13. 13.

    Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).

  14. 14.

    Nagelkerken, I., Goldenberg, S. U., Ferreira, C. M., Russell, B. D. & Connell, S. D. Species interactions drive fish biodiversity loss in a high-CO2 world. Curr. Biol. 27, 2177–2184 (2017).

  15. 15.

    Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).

  16. 16.

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

  17. 17.

    Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).

  18. 18.

    Nagelkerken, I. & Connell, S. D. Global alteration of ocean ecosystem functioning due to increasing human CO2 emissions. Proc. Natl Acad. Sci. USA 112, 13272–13277 (2015).

  19. 19.

    Pistevos, J. C. A., Nagelkerken, I., Rossi, T., Olmos, M. & Connell, S. D. Ocean acidification and global warming impair shark hunting behaviour and growth. Sci. Rep. 5, 16293 (2015).

  20. 20.

    Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).

  21. 21.

    Portner, H. O. Ecosystem effects of ocean acidification in times of ocean warming: a physiologist’s view. Mar. Ecol. Prog. Ser. 373, 203–217 (2008).

  22. 22.

    Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).

  23. 23.

    Nagelkerken, I., Russell, B. D., Gillanders, B. M. & Connell, S. D. Ocean acidification alters fish populations indirectly through habitat modification. Nat. Clim. Change 6, 89–93 (2016).

  24. 24.

    Connell, S. D. et al. How ocean acidification can benefit calcifiers. Curr. Biol. 27, R95–R96 (2017).

  25. 25.

    Duffy, J. E. et al. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecol. Lett. 10, 522–538 (2007).

  26. 26.

    Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).

  27. 27.

    Wong, B. B. M. & Candolin, U. Behavioral responses to changing environments. Behav. Ecol. 26, 665–673 (2015).

  28. 28.

    Hartman, E. J & Abrahams, M. V. Sensory compensation and the detection of predators: the interaction between chemical and visual information. Proc. R. Soc. B 267, 571–575 2000).

  29. 29.

    Devine, B. M., Munday, P. L. & Jones, G. P. Rising CO2 concentrations affect settlement behaviour of larval damselfishes. Coral Reefs 31, 229–238 (2012).

  30. 30.

    Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

  31. 31.

    Abrantes, K. G., Barnett, A. & Bouillon, S. Stable isotope-based community metrics as a tool to identify patterns in food web structure in east African estuaries. Funct. Ecol. 28, 270–282 (2014).

  32. 32.

    Munday, P. L., Warner, R. R., Monro, K., Pandolfi, J. M. & Marshall, D. J. Predicting evolutionary responses to climate change in the sea. Ecol. Lett. 16, 1488–1500 (2013).

  33. 33.

    Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–371 (2005).

  34. 34.

    Goldenberg, S. U., Nagelkerken, I., Ferreira, C. M., Ullah, H. & Connell, S. D. Boosted food web productivity through ocean acidification collapses under warming. Glob. Change Biol. 23, 4177–4184 (2017).

  35. 35.

    Sunday, J. M. et al. Ocean acidification can mediate biodiversity shifts by changing biogenic habitat. Nat. Clim. Change 7, 81–85 (2017).

  36. 36.

    Houston, A. I., McNamara, J. M. & Hutchinson, J. M. C. General results concerning the trade-off between gaining energy and avoiding predators. Phil. Trans. R. Soc. Lond. B 341, 375–397 (1993).

  37. 37.

    Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation — a review and prospectus. Can. J. Zool. 68, 619–640 1990).

  38. 38.

    Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).

  39. 39.

    Bryars, S. & Rowling, K. Benthic habitats of eastern Gulf St Vincent: major changes in benthic cover and composition following European settlement of Adelaide. Trans. R. Soc. S. Aust. 133, 318–338 (2009).

  40. 40.

    Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, New York, 2002).

  41. 41.

    Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).

  42. 42.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. B 57, 289–300 (1995).

  43. 43.

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

  44. 44.

    Hedges, L. V. Distribution theory for Glass’s estimator of effect size and related estimators. J. Educ. Stat. 6, 107–128 (1981).

  45. 45.

    Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis (John Wiley & Sons, Chichester, United Kingdom, 2009).

  46. 46.

    Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).

  47. 47.

    Knapp, G. & Hartung, J. Improved tests for a random effects meta-regression with a single covariate. Stat. Med. 22, 2693–2710 (2003).

  48. 48.

    Cochran, W. G. The combination of estimates from different experiments. Biometrics 10, 101–129 (1954).

  49. 49.

    Duval, S. & Tweedie, R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 56, 455–463 (2000).

Download references


We thank all of the students, W. Hutchinson, M. Gluis, T. Kildea and M. Brustolin for their help with the mesocosm project. Financial support was received through the Australian Research Council Future Fellowship Grant FT120100183 (to I.N.) and FT0991953 (to S.D.C.) and through a grant from the Environment Institute (the University of Adelaide). C.M.F. was supported by a Science Without Borders PhD scholarship through CAPES (Coordination for the Improvement of Higher Education Personnel) Brazil (scholarship no. 13058134).

Author contributions

S.U.G., I.N. S.D.C and C.M.F designed the study, S.U.G., E.M., A.B. and C.M.F. performed the research, S.U.G. analysed the data, S.U.G. conducted the meta-analysis, S.U.G., I.N. and S.D.C. wrote the manuscript and all authors contributed to writing the manuscript.

Author information


  1. Southern Seas Ecology Laboratories, School of Biological Sciences and The Environment Institute, The University of Adelaide, Adelaide, South Australia, Australia

    • Silvan U. Goldenberg
    • , Ivan Nagelkerken
    • , Emma Marangon
    • , Angélique Bonnet
    • , Camilo M. Ferreira
    •  & Sean D. Connell


  1. Search for Silvan U. Goldenberg in:

  2. Search for Ivan Nagelkerken in:

  3. Search for Emma Marangon in:

  4. Search for Angélique Bonnet in:

  5. Search for Camilo M. Ferreira in:

  6. Search for Sean D. Connell in:

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ivan Nagelkerken.

Supplementary information

  1. Supplementary Information

    Supplementary Results, Supplementary Methods, Supplementary Tables 1–18, Supplementary Figure 1–7, Supplementary References

  2. Supplementary Table 19

    List of all experiments considered in the meta-analysis with their design characteristics