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A global synthesis of animal phenological responses to climate change

A Publisher Correction to this article was published on 21 February 2018

This article has been updated


Shifts in phenology are already resulting in disruptions to the timing of migration and breeding, and asynchronies between interacting species1,2,3,4,5. Recent syntheses have concluded that trophic level1, latitude6 and how phenological responses are measured7 are key to determining the strength of phenological responses to climate change. However, researchers still lack a comprehensive framework that can predict responses to climate change globally and across diverse taxa. Here, we synthesize hundreds of published time series of animal phenology from across the planet to show that temperature primarily drives phenological responses at mid-latitudes, with precipitation becoming important at lower latitudes, probably reflecting factors that drive seasonality in each region. Phylogeny and body size are associated with the strength of phenological shifts, suggesting emerging asynchronies between interacting species that differ in body size, such as hosts and parasites and predators and prey. Finally, although there are many compelling biological explanations for spring phenological delays, some examples of delays are associated with short annual records that are prone to sampling error. Our findings arm biologists with predictions concerning which climatic variables and organismal traits drive phenological shifts.

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Fig. 1: Improving how we understand advancements in phenology due to climate change.
Fig. 2: The uneven global distribution of published studies exploring the phenology of animals.
Fig. 3: The ability of phenology to track temperature varies among taxonomic classes of animals, ecto- or endothermy, and trophic level.

Change history

  • 21 February 2018

    In the PDF version of this Letter originally published, Fig. 3 was a duplicate of Fig. 1. This has now been corrected. The HTML version was unaffected.


  1. 1.

    Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).

    CAS  Article  Google Scholar 

  3. 3.

    Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).

    CAS  Article  Google Scholar 

  4. 4.

    Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).

    CAS  Article  Google Scholar 

  5. 5.

    Ge, Q. S., Wang, H. J., Rutishauser, T. & Dai, J. H. Phenological response to climate change in China: a meta-analysis. Glob. Chang. Biol. 21, 265–274 (2015).

    Article  Google Scholar 

  6. 6.

    While, G. M. & Uller, T. Quo vadis amphibia? Global warming and breeding phenology in frogs, toads and salamanders. Ecography 37, 921–929 (2014).

    Article  Google Scholar 

  7. 7.

    Brown, C. J. et al. Ecological and methodological drivers of species’ distribution and phenology responses to climate change. Glob. Chang. Biol. 22, 1548–1560 (2016).

    Article  Google Scholar 

  8. 8.

    Rosenzweig, C. et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 453, 353–357 (2008).

    CAS  Article  Google Scholar 

  9. 9.

    Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).

    Article  Google Scholar 

  10. 10.

    Barbraud, C. & Weimerskirch, H. Antarctic birds breed later in response to climate change. Proc. Natl Acad. Sci. USA 103, 6248–6251 (2006).

    CAS  Article  Google Scholar 

  11. 11.

    Both, C. et al. Avian population consequences of climate change are most severe for long-distance migrants in seasonal habitats. Proc. R. Soc. B 277, 1259–1266 (2010).

    Article  Google Scholar 

  12. 12.

    Hegland, S. J., Nielsen, A., Lazaro, A., Bjerknes, A. L. & Totland, O. How does climate warming affect plant-pollinator interactions? Ecol. Lett. 12, 184–195 (2009).

    Article  Google Scholar 

  13. 13.

    Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    Visser, M. E. & Holleman, L. J. M. Warmer springs disrupt the synchrony of oak and winter moth phenology. Proc. R. Soc. B 268, 289–294 (2001).

    CAS  Article  Google Scholar 

  15. 15.

    McKinney, A. M. et al. Asynchronous changes in phenology of migrating broad-tailed hummingbirds and their early-season nectar resources. Ecology 93, 1987–1993 (2012).

    Article  Google Scholar 

  16. 16.

    Mas-Coma, S., Valero, M. A. & Bargues, M. D. Climate change effects on trematodiases, with emphasis on zoonotic fascioliasis and schistosomiasis. Vet. Parasitol. 163, 264–280 (2009).

    Article  Google Scholar 

  17. 17.

    Yu, H. Y., Luedeling, E. & Xu, J. C. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).

    CAS  Article  Google Scholar 

  18. 18.

    Wolkovich, E. M., Cook, B. I. & Davies, T. J. Progress towards an interdisciplinary science of plant phenology: Building predictions across space, time and species diversity. New Phytol. 201, 1156–1162 (2014).

    Article  Google Scholar 

  19. 19.

    Lajeunesse, M. J. On the meta-analysis of response ratios for studies with correlated and multi-group designs. Ecology 92, 2049–2055 (2011).

    Article  Google Scholar 

  20. 20.

    van Houwelingen, H. C., Arends, L. R. & Stijnen, T. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat. Med. 21, 589–624 (2002).

    Article  Google Scholar 

  21. 21.

    Inouye, D. W., Barr, B., Armitage, K. B. & Inouye, B. D. Climate change is affecting altitudinal migrants and hibernating species. Proc. Natl Acad. Sci. USA 97, 1630–1633 (2000).

    CAS  Article  Google Scholar 

  22. 22.

    Lawrimore, J. H. et al. An overview of the global historical climatology network monthly mean temperature data set, version 3. J. Geophys. Res. Atmos. 116, D19121 (2011).

    Article  Google Scholar 

  23. 23.

    Lajeunesse, M. J. Meta-analysis and the comparative phylogenetic method. Am. Nat. 174, 369–381 (2009).

    Google Scholar 

  24. 24.

    Field, C. B. & Van Aalst, M. Climate Change 2014: Impacts, Adaptation, and Vulnerability Section 1 (eds Field, C. B. et al.) (IPCC, Cambridge Univ. Press, 2014).

  25. 25.

    Paternoster, R., Brame, R., Mazerolle, P. & Piquero, A. Using the correct statistical test for the equality of regression coefficients. Criminology 36, 859–866 (1998).

    Article  Google Scholar 

  26. 26.

    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).

    CAS  Article  Google Scholar 

  27. 27.

    Ovaskainen, O. et al. Community-level phenological response to climate change. Proc. Natl Acad. Sci. USA 110, 13434–13439 (2013).

    CAS  Article  Google Scholar 

  28. 28.

    Lehikoinen, E., Sparks, T. H. & Zalakevicius, M. Arrival and departure dates. Adv. Ecol. Res. 35, 1–31 (2004).

    Article  Google Scholar 

  29. 29.

    Gordo, O. Why are bird migration dates shifting? A review of weather and climate effects on avian migratory phenology. Clim. Res. 35, 37–58 (2007).

    Article  Google Scholar 

  30. 30.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (Cambridge Univ. Press, 2015).

  31. 31.

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

    Article  Google Scholar 

  32. 32.

    Dunning, J. B. Jr CRC Handbook of Avian Body Masses (CRC, Boca Raton, Florida, 1992).

  33. 33.

    Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).

    CAS  Article  Google Scholar 

  34. 34.

    Garcia-Barros, E. Body size, egg size, and their interspecific relationships with ecological and life history traits in butterflies (Lepidoptera: Papilionoidea, Hesperioidea). Biol. J. Linn. Soc. 70, 251–284 (2000).

    Article  Google Scholar 

  35. 35.

    Karlsson, B. Resource allocation and mating systems in butterflies. Evolution 49, 955–961 (1995).

    Article  Google Scholar 

  36. 36.

    Trochet, A. et al. A database of life-history traits of European amphibians. Biodivers. Data J. 2, e4123 (2014).

    Article  Google Scholar 

  37. 37.

    Brose, U. Body sizes of consumers and their resources: Ecological archives E086-135. Ecology 86, 2545–2545 (2005).

    Article  Google Scholar 

  38. 38.

    Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090-184. Ecology 90, 2648–2648 (2009).

    Article  Google Scholar 

  39. 39.

    Myers, P. et al. The Animal Diversity Web (2016);

  40. 40.

    Williams, R. N. & MacGowan, B. J. in Proc. Indiana Acad. Sci. (eds Hay, O. P. et al.) 147–150 (1891).

  41. 41.

    Chown, S. L. et al. Scaling of insect metabolic rate is inconsistent with the nutrient supply network model. Funct. Ecol. 21, 282–290 (2007).

    Article  Google Scholar 

  42. 42.

    Hódar, J. The use of regression equations for the estimation of prey length and biomass in diet studies of insectivore vertebrates. Miscell. Zool. 20, 1–10 (1997).

    Google Scholar 

  43. 43.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2009).

  44. 44.

    Viechtbauer, W. Conducting meta-analyses in with the metafor package. J. Statistical Softw. 36, 3 (2010).

    Article  Google Scholar 

  45. 45.

    Olkin, I. & Finn, J. D. Correlations redux. Psychol. Bull. 118, 155–164 (1995).

    Article  Google Scholar 

  46. 46.

    Becker, B. J. in Handbook of Applied Multivariate Statistics and Mathematical Modeling (eds Tinsley, H. & Brown, S.) 499–526 (Academic, Cambridge, MA, 2000).

  47. 47.

    Higham, N. J. Computing the nearest correlation matrix—A problem from finance. IMA J. Numer. Anal. 22, 329–343 (2002).

    Article  Google Scholar 

  48. 48.

    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    CAS  Article  Google Scholar 

  49. 49.

    Grafen, A. The phylogenetic regression. Phil. Trans. R. Soc. B 326, 119–157 (1989).

    CAS  Article  Google Scholar 

  50. 50.

    Hedges, S. B., Dudley, J. & Kumar, S. TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics 22, 2971–2972 (2006).

    CAS  Article  Google Scholar 

  51. 51.

    Betancur-R, R. et al. The tree of life and a new classification of bony fishes. PLOS Current. Tree Life (2013).

  52. 52.

    Meredith, R. W. et al. Impacts of the Cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334, 521–524 (2011).

    CAS  Article  Google Scholar 

  53. 53.

    Shaffer, H. B. & McKnight, M. L. The polytypic species revisited: Genetic differentiation and molecular phylogenetics of the tiger salamander Ambystoma tigrinum (Amphibia: Caudata) complex. Evolution 50, 417–433 (1996).

    CAS  Article  Google Scholar 

  54. 54.

    Moriarty, E. C. & Cannatella, D. C. Phylogenetic relationships of the North American chorus frogs (Pseudacris: Hylidae). Mol. Phylogenet Evol. 30, 409–420 (2004).

    CAS  Article  Google Scholar 

  55. 55.

    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    CAS  Article  Google Scholar 

  56. 56.

    Podar, M., Haddock, S. H. D., Sogin, M. L. & Harbison, G. R. A molecular phylogenetic framework for the phylum Ctenophora using 18S rRNA genes. Mol. Phylogenet Evol. 21, 218–230 (2001).

    CAS  Article  Google Scholar 

  57. 57.

    Regier, J. C. et al. A large-scale, higher-level, molecular phylogenetic study of the insect order Lepidoptera (moths and butterflies). PLoS ONE 8, e58568 (2013).

    CAS  Article  Google Scholar 

  58. 58.

    Trautwein, M. D., Wiegmann, B. M., Beutel, R., Kjer, K. M. & Yeates, D. K. Advances in insect phylogeny at the dawn of the postgenomic era. Annu. Rev. Entomol. 57, 449–44 (2012).

    CAS  Article  Google Scholar 

  59. 59.

    Wahlberg, N. et al. Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers. Proc. R. Soc. B 272, 1577–1586 (2005).

    CAS  Article  Google Scholar 

  60. 60.

    Freitas, A. V. L. & Brown, K. S. Phylogeny of the Nymphalidae (Lepidoptera). Syst. Biol. 53, 363–383 (2004).

    Article  Google Scholar 

  61. 61.

    Dumont, H. J., Vierstraete, A. & Vanfleteren, J. R. A molecular phylogeny of the Odonata (Insecta). Syst. Entomol. 35, 6–18 (2010).

    Article  Google Scholar 

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We thank N. Argento and C. Gionet for assistance extracting data from studies, T. James for assistance compiling references, C. Parmesan for helpful discussions on vernalization and phenological meta-analyses in general, and D. Civitello, B. Delius, N. Halstead, S. Knutie, K. Nguyen, N. Ortega, B. Roznik, E. Sauer and S. Young for comments that resulted in significant improvements to the manuscript. This research was supported by grants from the National Science Foundation to M.J.L (DBI-1262545, DEB-1451031) and J.R.R. (EF-1241889, DEB-1518681) and National Institutes of Health (R01GM109499, R01TW010286), US Department of Agriculture (NRI 2006–01370, 2009-35102-0543) and US Environmental Protection Agency (CAREER 83518801) to J.R.R.

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J.M.C., M.J.L., and J.R.R. contributed ideas and devised the analyses. J.M.C. assembled the database of phenological time-series and collected climate data. M.J.L. designed and conducted the analyses. J.M.C., M.J.L. and J.R.R. wrote the paper.

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Correspondence to Jeremy M. Cohen.

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Supplementary Discussion, Supplementary Figures 1–6, Supplementary Tables 1–8, Supplementary Code, Supplementary References and PRISMA Checklist

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Cohen, J.M., Lajeunesse, M.J. & Rohr, J.R. A global synthesis of animal phenological responses to climate change. Nature Clim Change 8, 224–228 (2018).

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