Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification


Predicting the response of marine animals to climate change is hampered by a lack of multigenerational studies on evolutionary adaptation, particularly to combined ocean warming and acidification (OWA). We provide evidence for rapid adaptation to OWA in the foundational copepod species, Acartia tonsa, by assessing changes in population fitness on the basis of a comprehensive suite of life-history traits, using an orthogonal experimental design of nominal temperature (18 °C, 22 °C) and \(p_{\mathrm{{CO}}_2}\) (400, 2,000 µatm) for 25 generations (~1 year). Egg production and hatching success initially decreased under OWA, resulting in a 56% reduction in fitness. However, both traits recovered by the third generation, and average fitness was reduced thereafter by only 9%. Antagonistic interactions between warming and acidification in later generations decreased survival, thereby limiting full fitness recovery. Our results suggest that such interactions constrain evolutionary rescue and add complexity to predictions of the responses of animal populations to climate change.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Changes in EPR and HS during the transgenerational experiment.
Fig. 2: Changes in survival during the transgenerational study.
Fig. 3: Mean fitness values, λ, calculated for the transgenerational study.
Fig. 4: Fitness landscapes showing trait contribution to adaptation during the transgenerational experiment.

Data availability

The phenotypic and physical data referred to in the text are deposited in Zenodo ( The genetic diversity data are deposited in GenBank: BioProject number PRJNA590963. Source data are provided with this paper.

Code availability

The scripts for analysis of the physical, phenotypic and genetic diversity data are deposited in Zenodo (


  1. Hönisch, B. et al. The geological record of ocean acidification. Science 335, 1058–1063 (2012).

    Article  CAS  Google Scholar 

  2. Bindoff, N. L. et al. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 447–588 (IPCC, 2019).

  3. Pörtner, H.-O. et al. in Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) 35–74 (IPCC, 2019).

  4. Caldeira, K. & Wickett, M. E. Anthropogenic carbon and ocean pH. Nature 425, 365 (2003).

    CAS  Article  Google Scholar 

  5. Cai, W. J. et al. Acidification of subsurface coastal waters enhanced by eutrophication. Nat. Geosci. 4, 766–770 (2011).

    CAS  Article  Google Scholar 

  6. Wallace, R. B., Baumann, H., Grear, J. S., Aller, R. C. & Gobler, C. J. Coastal ocean acidification: the other eutrophication problem. Estuar. Coast. Shelf Sci. 148, 1–13 (2014).

    CAS  Article  Google Scholar 

  7. 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).

    Article  Google Scholar 

  8. Schlichting, C. D. & Pigliucci, M. Phenotypic Evolution: A Reaction Norm Perspective (Sinauer Associates, 1998).

  9. Kelly, M. W. & Hofmann, G. E. Adaptation and the physiology of ocean acidification. Funct. Ecol. 27, 980–990 (2013).

    Article  Google Scholar 

  10. Pespeni, M. H. et al. Evolutionary change during experimental ocean acidification. Proc. Natl Acad. Sci. USA 110, 6937–6942 (2013).

    CAS  Article  Google Scholar 

  11. Thor, P. & Dupont, S. Transgenerational effects alleviate severe fecundity loss during ocean acidification in a ubiquitous planktonic copepod. Glob. Change Biol. 21, 2261–2271 (2015).

    Article  Google Scholar 

  12. Donelson, J. M., Salinas, S., Munday, P. L. & Shama, L. N. S. Transgenerational plasticity and climate change experiments: where do we go from here? Glob. Change Biol. 24, 13–34 (2018).

    Article  Google Scholar 

  13. Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).

    Article  CAS  Google Scholar 

  14. Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).

  15. Byrne, M. in Oceanography and Marine Biology: An Annual Review Vol. 49 (eds Gibson, R. N. et al.) Ch. 1 (CRC Press, 2011).

  16. Whiteley, N. M. Physiological and ecological responses of crustaceans to ocean acidification. Mar. Ecol. Prog. Ser. 430, 257–271 (2011).

    CAS  Article  Google Scholar 

  17. Cripps, G., Lindeque, P. & Flynn, K. J. Have we been underestimating the effects of ocean acidification in zooplankton? Glob. Change Biol. 20, 3377–3385 (2014).

    Article  Google Scholar 

  18. Baumann, H. Experimental assessments of marine species sensitivities to ocean acidification and co-stressors: how far have we come? Can. J. Zool. 97, 399–408 (2019).

    Article  Google Scholar 

  19. Gibbin, E. M. et al. Can multi-generational exposure to ocean warming and acidification lead to the adaptation of life history and physiology in a marine metazoan? J. Exp. Biol. 220, 551–563 (2017).

    Google Scholar 

  20. Gibbin, E. M., Massamba N’Siala, G., Chakravarti, L. J., Jarrold, M. D. & Calosi, P. The evolution of phenotypic plasticity under global change. Sci. Rep. 7, 17253 (2017).

    Article  CAS  Google Scholar 

  21. Gonzalez, A., Ophelie, R., Ferriere, R. & Hochberg, M. E. Evolutionary rescue: an emerging focus at the intersection between ecology and evolution. Philos. Trans. R. Soc. Lond. B 368, 20120404 (2012).

    Article  Google Scholar 

  22. Bell, G. & Gonzalez, A. Evolutionary rescue can prevent extinction following environmental change. Ecol. Lett. 12, 942–948 (2009).

    Article  Google Scholar 

  23. Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).

    Article  Google Scholar 

  24. Hardy, A. The Open Sea: The World of Plankton (Fontana Collins, 1970).

  25. Huys, R. & Boxshall, G. A. Copepod Evolution (The Ray Society, 1991).

  26. Beaugrand, G. & Reid, P. C. Long-term changes in phytoplankton, zooplankton and salmon related to climate. Glob. Change Biol. 9, 801–817 (2003).

    Article  Google Scholar 

  27. Möllmann, C., Müller-Karulis, B., Kornilovs, G. & St John, M. A. Effects of climate and overfishing on zooplankton dynamics and ecosystem structure: regime shifts, trophic cascade, and feedback loops in a simple ecosystem. ICES J. Mar. Sci. 65, 302–310 (2008).

    Article  Google Scholar 

  28. Steinberg, D. K. & Landry, M. R. Zooplankton and the ocean carbon cycle. Annu. Rev. Mar. Sci. 9, 413–444 (2017).

    Article  Google Scholar 

  29. Mauchline, J. (ed.) The Biology of Calanoid Copepods (Academic Press, 1998).

  30. Turner, J. T. The Feeding Ecology of Some Zooplankters That Are Important Prey Items of Larval Fish. NOAA NMFS Technical Report (1984).

  31. Rice, E., Dam, H. G. & Stewart, G. Impact of climate change on estuarine zooplankton: surface water warming in Long Island Sound is associated with changes in copepod size and community structure. Estuaries Coast 38, 13–23 (2015).

    Article  Google Scholar 

  32. Gobler, C. J. & Baumann, H. Hypoxia and acidification in marine ecosystems: coupled dynamics and effects on ocean life. Biol. Lett. 12, 20150976 (2016).

    Article  CAS  Google Scholar 

  33. Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. Lond. B 283, 20152592 (2016).

    Google Scholar 

  34. Burt, A. Perspective: the evolution of fitness. Evolution 49, 1–8 (1995).

    Google Scholar 

  35. Hendry, A. P. & Gonzalez, A. Whither adaptation? Biol. Philos. 23, 673–699 (2008).

    Article  Google Scholar 

  36. Arnold, S. J., Pfrender, M. E. & Jones, A. G. The adaptive landscape as a conceptual bridge between micro- and macroevolution. Genetica 112–113, 9–32 (2001).

    Article  Google Scholar 

  37. Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation (Sinauer Associates, 2001).

  38. Sasaki, M. C. & Dam, H. G. Integrating patterns of thermal tolerance and phenotypic plasticity with population genetics to improve understanding of vulnerability to warming in a widespread copepod. Glob. Change Biol. 25, 4147–4164 (2019).

    Article  Google Scholar 

  39. Luikart, G., England, P. R., Tallmon, D., Jordan, S. & Taberlet, P. The power and promise of population genomics: from genotyping to genome typing. Nat. Rev. Genet. 4, 981–994 (2003).

    CAS  Article  Google Scholar 

  40. Black, W. C. IV, Baer, C. F., Antolin, M. F. & DuTeau, N. M. Population genomics: genome-wide sampling of insect populations. Annu. Rev. Entomol. 46, 441–469 (2001).

    CAS  Article  Google Scholar 

  41. Brennan, R. et al. Loss and recovery of transcriptional plasticity after long-term adaptation to global change conditions in a marine copepod. Preprint at bioRxiv (2020).

  42. Kingsolver, J. G. & Pfennig, D. W. Patterns and power of phenotypic selection in nature. Bioscience 57, 561–572 (2007).

    Article  Google Scholar 

  43. Crespi, B. J. & Bookstein, F. L. A path-analytic model for the measurement of selection on morphology. Evolution 43, 18–28 (1989).

    Article  Google Scholar 

  44. Pigliucci, M. & Kaplan, J. Making Sense of Evolution (Univ. Chicago Press, 2006);

  45. Bush, A. et al. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19, 1468–1478 (2016).

    Article  Google Scholar 

  46. Riebesell, U. & Gattuso, J. Lessons learned from ocean acidification research. Nat. Clim. Change 5, 2014–2016 (2015).

    Article  CAS  Google Scholar 

  47. Langer, J. A. F., Meunier, C. L., Ecker, U. & Horn, H. G. Acclimation and adaptation of the coastal calanoid copepod Acartia tonsa to ocean acidification: a long-term laboratory investigation. Mar. Ecol. Prog. Ser. 619, 35–51 (2019).

    CAS  Article  Google Scholar 

  48. De Wit, P., Dupont, S. & Thor, P. Selection on oxidative phosphorylation and ribosomal structure as a multigenerational response to ocean acidification in the common copepod Pseudocalanus acuspes. Evol. Appl. 9, 1112–1123 (2016).

    Article  CAS  Google Scholar 

  49. Chakravarti, L. J. et al. Can trans-generational experiments be used to enhance species resilience to ocean warming and acidification? Evol. Appl. 9, 1133–1146 (2016).

    CAS  Article  Google Scholar 

  50. Carrier-Belleau, C., Drolet, D., McKindsey, C. W. & Archambault, P. Environmental stressors, complex interactions and marine benthic communities’ responses. Sci. Rep. 11, 4194 (2021).

    CAS  Article  Google Scholar 

  51. Dam, H. G. & Baumann, H. in Climate Change Impacts on Fisheries and Aquaculture: A Global Analysis (eds Phillips, B. F. and Pérez-Ramírez, M.) 851–874 (Wiley, 2017).

  52. Bell, G. Evolutionary rescue and the limits of adaptation. Philos. Trans. R. Soc. Lond. B 368, 20120080 (2013).

    Article  Google Scholar 

  53. Falconer, D. S. Introduction to Quantitative Genetics (Longman Scientific and Technical, 1989).

  54. Angilletta, M. J. Jr Estimating and comparing thermal performance curves. J. Therm. Biol. 31, 541–545 (2006).

    Article  Google Scholar 

  55. Feinberg, L. R. & Dam, H. G. Effects of diet on dimensions, density and sinking rates of fecal pellets of the copepod Acartia tonsa. Mar. Ecol. Prog. Ser. 175, 87–96 (1998).

    Article  Google Scholar 

  56. Pierrot, D., Lewis, E. & Wallace, D. W. R. MS Excel Program Developed for CO2 System Calculations. ORNL/CDIAC-105a. (Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, 2006);

  57. Lueker, T. J., Dickson, A. G. & Keeling, C. D. Ocean \(p_{{\mathrm{CO}}_2}\) calculated from dissolved inorganic carbon, alkalinity, and equations for K1 and K2: validation based on laboratory measurements of CO2 in gas and seawater at equilibrium. Mar. Chem. 70, 105–119 (2000).

  58. Dickson, A. G. Standard potential of the reaction: AgCl(s) + 12H2 (g) = Ag(s) + HCl (aq), and the standard acidity constant of the ion HSO4 in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22, 113–127 (1990).

    CAS  Article  Google Scholar 

  59. Uppström, L. R. The boron/chlorinity ratio of deep-sea water from the Pacific Ocean. Deep Sea Res. Oceanogr. Abstr. 21, 161–162 (1974).

    Article  Google Scholar 

  60. Murray, C. S. & Baumann, H. You better repeat it: complex CO2× temperature effects in Atlantic silverside offspring revealed by serial experimentation. Diversity 10, 69 (2018).

    CAS  Article  Google Scholar 

  61. Schank, J. C. & Koehnle, T. J. Pseudoreplication is a Pseudoproblem. J. Comp. Psychol. 123, 421–433 (2009).

    Article  Google Scholar 

  62. Oksanen, L. Logic of experiments in ecology: is pseudoreplication a pseudoissue? Oikos 94, 27–38 (2001).

    Article  Google Scholar 

  63. Therneau, T. A Package for Survival Analysis in R. R package 3.2-11 (2021);

  64. Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).

    Article  Google Scholar 

  65. Rosseel, Y. lvaan: an R package for structural equation modeling. J. Stat. Softw. (2012).

  66. Epskamp, S., Stuber, S., Nak, J., Veenman, M. & Jorgensen, T. D. semPlot: Path Diagrams and Visual Analysis of Various SEM Packages’ Output. (2019);

  67. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  Google Scholar 

  68. Jørgensen, T. S. et al. The genome and mRNA transcriptome of the cosmopolitan calanoid copepod Acartia tonsa Dana improve the understanding of copepod genome size evolution. Genome Biol. Evol. 11, 1440–1450 (2019).

    Article  CAS  Google Scholar 

  69. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at (2013).

  70. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  CAS  Google Scholar 

  71. Kofler, R. et al. Popoolation: a toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS One 6, e15925 (2011).

    CAS  Article  Google Scholar 

  72. R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020);

  73. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B 73, 3–36 (2011).

    Article  Google Scholar 

  74. Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 149 (2018).

    Article  Google Scholar 

  75. Dam, H. G. et al. Data and code repository for ‘Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification’. Zenodo (2021).

Download references


Research was supported by grants from the USA National Science Foundation (OCE-1559180 awarded to H.G.D., M.B.F. and H.B.; and OCE-1559075 awarded to M.H.P.) and Connecticut Sea Grant (R/LR‐25) awarded to H.G.D., M.B.F. and H.B. The authors thank W. Huffman for aiding in pilot experiments; C. Murray for assistance in alkalinity measurements; D. Arbige, C. Woods and B. Dziomba for help in maintaining equipment and constructing custom enclosures for the experiments; and T. Moore and J. Lee of UConn’s Statistical Consulting Services for advice and assistance on data analysis.

Author information

Authors and Affiliations



H.G.D. conceived the project, designed research, aided in data analysis and wrote the manuscript. J.A.deM. conducted experiments, analysed data, created figures and wrote the manuscript with H.G.D. G.P., L.N. and X.H. conducted experiments. M.B.F. conceived the project and designed research. H.B. conceived the project, designed research and designed the CO2 delivery system. R.S.B. performed genomic diversity analysis. M.H.P. conceived the project, designed research and performed genomic analysis. All authors edited and approved the paper.

Corresponding authors

Correspondence to Hans G. Dam or James A. deMayo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Peter Thor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Development time vs generation for transgenerational study.

Shown are the mean calculated development times (naupliar stage 1 to adult) for each treatment at each generation where life-history traits are measured. Curves for treatments are offset for clarity. Treatment colors: blue: AM; green: OA; orange: OW; brown: OWA. Box and whisker plots for these data are available in Supplemental Fig. 4.

Source data

Extended Data Fig. 2 Sex ratio vs generation for transgenerational study.

Results for sex ratio across generations modeled as A) linear model and B) Generalized Additive Model. Treatment colors: blue: AM; green: OA; orange: OW; brown: OWA.

Source data

Extended Data Fig. 3 Frequency distribution of population fitness values (λ) for the four treatments in the transgenerational experiment.

Treatment colors: blue: AM; green: OA; orange: OW; brown: OWA.

Source data

Extended Data Fig. 4 Predicted probabilities of non-zero fitness (lambda) values vs generations across treatments in the transgenerational experiment.

Shown are predicted mean non-zero lambda probabilities. Probabilities for ambient (AM), ocean acidification (OA), and ocean warming (OW) treatments are statistically independent of generations. Probabilities for the simultaneous ocean warming and acidification (OWA) significantly increase with generation. Shading represents 95% confidence intervals around the mean. Treatment colors: blue: AM; green: OA; orange: OW; brown: OWA.

Source data

Extended Data Fig. 5 Estimates of genetic diversity (π) at generation 25 vs treatments of the transgenerational experiment.

Estimates were calculated in 100 bp non-overlapping sliding windows. Windows were included when at least 50% of sites had coverage between 30x and 1000x per sample and the window was covered across all samples. The asterisk indicates the sample in the OA treatment with reduced genetic diversity relative to other samples (Wilcoxon Rank Sum test with Holm correction for multiple testing; p < 0.05); all other samples were not significantly different (p > 0.05). In the boxes, the centre black line represents the median, the circles represent means, upper box edge represents the 75% quartile, lower box edge represents 25% quartile, whiskers represent 1.5x interquartile range, and points represent outliers. Treatment colors: blue: AM; green: OA; orange: OW; brown: OWA.

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–4 and Tables 1–5.

Reporting Summary

Source data

Source Data Fig. 1

Unprocessed values of egg production and hatching success.

Source Data Fig. 2

Unprocessed values for survival.

Source Data Fig. 3

Unprocessed values for fitness (λ).

Source Data Fig. 4

Sheet 1: Table of fitness (λ) and relative fitness values with corresponding survival, egg production, hatching success, development time and sex ratio values for the first and last evaluated generations. Sheet 2: Table of survival values for the first and last generations.

Source Data Extended Data Fig. 1

Unprocessed values for calculated development time.

Source Data Extended Data Fig. 2

Unprocessed values for observed sex ratio (proportion of females relative to males).

Source Data Extended Data Fig. 3

Unprocessed values for fitness (λ).

Source Data Extended Data Fig. 4

Unprocessed values for fitness (λ) with binary transformed λ. λ values > 0 are given a binary value of 1, and λ values = 0 are given a binary value of 0.

Source Data Extended Data Fig. 5

Unprocessed values of nucleotide diversity for F25.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dam, H.G., deMayo, J.A., Park, G. et al. Rapid, but limited, zooplankton adaptation to simultaneous warming and acidification. Nat. Clim. Chang. 11, 780–786 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing