Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Phenology varies with phylogeny but not by trophic level with climate change

Nature Ecology & Evolution volume 8, pages 1889–1896 (2024)Cite this article

Subjects

Abstract

Shifts in phenology with climate change can lead to asynchrony between interacting species, with cascading impacts on ecosystem services. Previous meta-analyses have produced conflicting results on whether asynchrony has increased in recent decades, but the underlying data have also varied—including in species composition, interaction types and whether studies compared data grouped by trophic level or compared shifts in known interacting species pairs. Here, using updated data from previous studies and a Bayesian phylogenetic model, we found that species have advanced an average of 3.1 days per decade across 1,279 time series across 29 taxonomic classes. We found no evidence that shifts vary by trophic level: shifts were similar when grouped by trophic level, and for species pairs when grouped by their type of interaction—either as paired species known to interact or as randomly paired species. Phenology varied with phylogeny (λ = 0.4), suggesting that uneven sampling of species may affect estimates of phenology and potentially phenological shifts. These results could aid forecasting for well-sampled groups but suggest that climate change has not yet led to widespread increases in phenological asynchrony across interacting species, although substantial biases in current data make forecasting for most groups difficult.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Estimating shifts in synchrony may—or may not—vary between known interacting species or randomly paired species (where data come from single-species time series that were often not collected originally for questions of synchrony).
Fig. 2: Time series analysis includes globally distributed and spanning the tree of life.
Fig. 3: Phenological shifts were similar across species trophic levels and functional groups.
Fig. 4: Changes in phenological synchrony were similar across known interacting pairs of species (shown in blue, with n given) and randomly paired species (shown in red) across different interaction types.
Fig. 5: Current estimated shifts in phenology, as represented by the solid lines, suggest most species have not been pushed beyond their pre-1980 variation in phenology (solid lines do not exceed their pre-existing variation, the 95% range of the quantile of which is represented by the coloured bands).

Similar content being viewed by others

Data availability

All data we scraped is available through the Knowledge Network for Biocomplexity (https://doi.org/10.5063/F12J69B2)56.

Code availability

Code developed for this analysis is available through the Knowledge Network for Biocomplexity (https://doi.org/10.5063/F12J69B2)56.

References

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

    Article  CAS  PubMed  Google Scholar 

  2. Parmesan, C. Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob. Chang. Biol. 13, 1860–1872 (2007).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  5. Vitasse, Y. et al. Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Rev. 96, 1816–1835 (2021).

    Article  PubMed  Google Scholar 

  6. Stenseth, N. C. & Mysterud, A. Climate, changing phenology, and other life history traits: nonlinearity and match–mismatch to the environment. Proc. Natl Acad. Sci. USA 99, 13379–13381 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).

    Article  Google Scholar 

  8. Lindén, A. Adaptive and nonadaptive changes in phenological synchrony. Proc. Natl Acad. Sci. USA 115, 5057–5059 (2018).

    Article  PubMed  Google Scholar 

  9. Forrest, J. R. K. Complex responses of insect phenology to climate change. Curr. Opin. Insect Sci. 17, 49–54 (2016).

    Article  PubMed  Google Scholar 

  10. Beard, K. H., Kelsey, K. C., Leffler, A. J. & Welker, J. M. The missing angle: ecosystem consequences of phenological mismatch. Trends Ecol. Evol. 34, 885–888 (2019).

    Article  PubMed  Google Scholar 

  11. Fang, J., Lutz, J. A., Wang, L., Shugart, H. H. & Yan, X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. Glob. Chang. Biol. 26, 6974–6988 (2020).

    Article  PubMed  Google Scholar 

  12. Gu, H. et al. Warming-induced increase in carbon uptake is linked to earlier spring phenology in temperate and boreal forests. Nat. Commun. 13, 1–8 (2022).

    CAS  Google Scholar 

  13. Visser, M. E. E. et al. Warmer springs lead to mistimes reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).

    Article  Google Scholar 

  14. Cresswell, W. & McCleery, R. How great tits maintain synchronization of their hatch date with food supply in response to long-term variability in temperature. J. Anim. Ecol. 72, 356–366 (2003).

    Article  Google Scholar 

  15. Visser, M. E., Lindner, M., Gienapp, P., Long, M. C. & Jenouvrier, S. Recent natural variability in global warming weakened phenological mismatch and selection on seasonal timing in great tits (Parus major). Proc. R. Soc. B 288, 1–10 (2021).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  18. Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).

    Article  CAS  PubMed  Google Scholar 

  19. Davis, C. C., Willis, C. G., Primack, R. B. & Miller-rushing, A. J. The importance of phylogeny to the study of phenological response to global climate change. Phil. Trans. R. Soc. Lond. B 365, 3201–3213 (2010).

    Article  Google Scholar 

  20. Philippart, C. J. et al. Climate-related changes in recruitment of the bivalve Macoma balthica. Limnol. Oceanogr. 48, 2171–2185 (2003).

    Article  Google Scholar 

  21. Lane, J. E., Kruuk, L. E., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–558 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).

    Article  Google Scholar 

  23. Cushing, D. H. The regularity of the spawning season of some fishes. ICES J. Mar. Sci. 33, 81–92 (1969).

    Article  Google Scholar 

  24. Cushing, D. H. Plankton production and year-class strength in fish populations: an update of the match/mismatch hypothesis. Adv. Mar. Biol. 26, 249–293 (1990).

    Article  Google Scholar 

  25. Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).

    Article  CAS  PubMed  Google Scholar 

  26. Paquette, A., Joly, S. & Messier, C. Explaining forest productivity using tree functional traits and phylogenetic information: two sides of the same coin over evolutionary scale? Ecol. Evol. 5, 1774–1783 (2015).

    Article  PubMed  Google Scholar 

  27. Arim, M. & Marquet, P. A. Intraguild predation: a widespread interaction related to species biology. Ecol. Lett. 7, 557–564 (2004).

    Article  Google Scholar 

  28. Williams, R. J. & Martinez, N. D. Limits to trophic levels and omnivory in complex food webs: theory and data. Am. Nat. 163, 458–468 (2004).

    Article  PubMed  Google Scholar 

  29. Lafferty, K. D., Dobson, A. P. & Kuris, A. M. Parasites dominate food web links. Proc. Natl Acad. Sci. USA 103, 11211–11216 (2006).

    Article  CAS  PubMed  Google Scholar 

  30. LaSalle, J. & Gauld, I. D. Parasitic Hymenoptera and the biodiversity crisis. Redia 74, 315–334 (1992).

    Google Scholar 

  31. Davies, T. J., Mcgill, B. J., Regetz, J. & Wolkovich, E. M. Phylogenetically weighted regression: a method for modelling non-stationarity on evolutionary trees. Glob. Ecol. Biogeogr. 28, 275–285 (2019).

    Article  Google Scholar 

  32. Prather, R. M. et al. Current and lagged climate affects phenology across diverse taxonomic groups. Proc. R. Soc. B 290, 1–11 (2023).

    Article  Google Scholar 

  33. Kraft, N. J. & Ackerly, D. D. Functional trait and phylogenetic tests of community assembly across spatial scales in an Amazonian forest. Ecol. Monogr. 80, 401–422 (2010).

    Article  Google Scholar 

  34. Best, R. J. & Stachowicz, J. J. Phenotypic and phylogenetic evidence for the role of food and habitat in the assembly of communities of marine amphipods. Ecology 95, 775–786 (2014).

    Article  PubMed  Google Scholar 

  35. Diamond, S. E. Contemporary climate-driven range shifts: putting evolution back on the table. Funct. Ecol. 32, 1652–1665 (2018).

    Article  Google Scholar 

  36. Descamps, S. et al. Diverging phenological responses of Arctic seabirds to an earlier spring. Glob. Chang. Biol. 25, 4081–4091 (2019).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  38. Prevey, J. et al. Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes. Glob. Chang. Biol. 23, 2660–2671 (2017).

    Article  PubMed  Google Scholar 

  39. Alecrim, E. F., Sargent, R. D. & Forrest, J. Higer-latitude spring-flowering herbs advance their phenology more than trees with warming temperatures. J. Ecol. 111, 156–169 (2023).

    Article  Google Scholar 

  40. Thackeray, S. J. et al. Food web de-synchronisation in England’s largest lake: an assessment based uponmmultiple phenological metrics. Glob. Chang. Biol. 19, 3568–3580 (2013).

    Article  PubMed  Google Scholar 

  41. Iler, A. M. et al. Maintenance of temporal synchrony between syrphid flies and floral resources despite differential phenological responses to climate. Glob. Chang. Biol. 19, 2348–2359 (2013).

    Article  PubMed  Google Scholar 

  42. Burgess, M. D. et al. Tritrophic phenological match-mismatch in space and time. Nat. Ecol. Evol. 2, 970–975 (2018).

    Article  PubMed  Google Scholar 

  43. Cayuela, L., Granzow-de la Cerda, Í., Albuquerque, F. S. & Golicher, D. J. Taxonstand: an r package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).

    Article  Google Scholar 

  44. Chamberlain, S. A. & Szöcs, E. taxize: taxonomic search and retrieval in R. F1000Res. 2, 1–30 (2013).

    Article  CAS  Google Scholar 

  45. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  46. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Smith, S. A. & O’Meara, B. C. TreePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228–232 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Navarro, J. C. A. et al. Amplification of Arctic warming by past air pollution reductions in Europe. Nat. Geosci. 9, 277–281 (2016).

    Article  Google Scholar 

  51. Morales-Castilla, I. et al. Phylogenetic estimates of species-level phenology improve ecological forecasting. Nat. Clim. Change (in the press).

  52. Gelman, A., Hill, J., & Vehtari, A. Regression and Other Stories (Cambridge Univ. Press, 2020).

  53. Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3. http://mc-stan.org/ (2018).

  54. R Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, 2020); https://www.R-project.org/

  55. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  56. Loughnan, D. et al. Data and code: phenology varies with phylogeny with similar shifts by trophic level with climate change. knb https://doi.org/10.5063/F12J69B2 (2024).

Download references

Acknowledgements

We thank the many researchers, citizen scientists and organizations who contributed data, including S. Thackeray, J. Cohen, Rothamsted Insect Survey, UK Phenology Network, Woodland Trust and Centre for Ecology & Hydrology T. J. Davies provided comments that improved the manuscript. Funding was provided by a Natural Sciences and Engineering Research Council (NSERC) Canada Graduate Scholarship-Doctoral award to D.L. and Canada Research Chair award in Temporal Ecology and NSERC Discovery awards to E.M.W.

Author information

Authors and Affiliations

  1. Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada

    Deirdre Loughnan, Geoffrey Legault & E. M. Wolkovich

  2. Montreal Botanical Garden, Montreal, Quebec, Canada

    Simon Joly

  3. Institut de recherche en biologie végétale, Université de Montréal, Montreal, Quebec, Canada

    Simon Joly

  4. Department of Biology, University of Ottawa, Ottawa, Ontario, Canada

    Heather M. Kharouba

  5. Symplectomorphic, LLC, New York, NY, USA

    Michael Betancourt

Authors
  1. Deirdre Loughnan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon Joly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geoffrey Legault
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heather M. Kharouba
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Betancourt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. M. Wolkovich
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization: D.L. and E.M.W.; methodology: D.L., E.M.W., G.L., S.J. and M.B.; critical insights: D.L., E.M.W., G.L., S.J. and M.B.; writing—original draft: D.L. and E.M.W.; writing—review and editing: D.L., E.M.W., H.M.K., G.L., S.J. and M.B.

Corresponding author

Correspondence to Deirdre Loughnan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Brian Inouye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Conceptual diagram of how phenological shifts by interacting species pairs effect species synchrony.

A conceptual diagram of how species within an interacting pair may differ in their phenological shifts and the resulting effects on species synchrony. a, Within an interacting pair, illustrated here using a consumer and resource species (shown in blue and red respectively) as an example (though our data includes five types of interactions), the resource species may shift at a greater, lesser, or equal rate than their consumer. b, This results in different synchrony responses, with negative asynchrony occurring when the consumers phenology shifts earlier (dotdash line), positive asynchrony occurring when the resources phenology shifts earlier relative to the consumer (dashed line), and no change when both species shift at the same rate (solid line).

Extended Data Fig. 2 The global distribution of our phenological time-series and lack of variation with latitudinal gradients.

Species time-series were observed across the globe, but with strong biases towards certain regions, a, with point colour representing the original source of the dataset and point size the number of species within a dataset. b, The observed trends in species phenological shifts do not vary with latitude despite spanning latitudinal gradients across temperate ecosystems (Table ED1), as shown using points coloured to depict the magnitude of species shift per decade.

Extended Data Fig. 3 Species pairs exhibiting variable shifts in phenology and the resulting changes in synchrony.

To assess how pairs of species differ in their phenological shifts and synchrony, we randomly sampled 1000 posterior estimates from both the high and low-level species within each interaction, as shown here for two sets of species pairs. a, Some pairs differ in their phenological shifts, as shown for the interaction between Parus major and it’s caterpillar prey, b, while others shift at similar rates, shown here for Accipiter nisus and Parus ater. c, Changes in synchrony are calculated as the differences between the high-level species, shown here as predators, and the low-level species, or prey.

Extended Data Fig. 4 Species phenological synchrony across suites of interactions when simulated for terrestrial and aquatic interactions separately.

Species phenological synchrony across a, pollination (n = 47), b, predation (n = 24), c, herbivory (n = 10), and d, competitive (n = 18) interactions amongst terrestrial species (a–c), in comparison to e, predation (n = 13), f, herbivory (n = 10), and g, competitive (n = 54) interactions amongst aquatic species (e–g), showed no differences in the distributions of posterior estimates of consumer or resource species phenological shifts for pairs of known interacting species (in blue) and randomly paired species (in red) for any of the four types of interactions. Plots are normalized by counts, with the x-axis spanning the total density distribution. Above the figures, the two lines depict the 90% uncertainty intervals (thinner line) and 50% interval (thicker line), and the point the median.

Extended Data Fig. 5 Estimated phenological shifts of the well-studied interaction between Parus major and caterpillars.

Our model replicates the strong asynchronies found in interactions of well studied species pairs. Illustrated here is the classic example of the estimated phenological shifts of Parus major and caterpillars, shown across each type of phenological event represented in our dataset.

Extended Data Fig. 6 Species phenological shifts across trophic levels, consumer types, habitats, and types of phenological events.

Shifts in species phenology are similar across several grouping factors, in- cluding: a, trophic levels, b, consumer types, and c, habitat types. d, We also observed similar shifts across phenological events, with the exception of juvenile bird first ap- pearance. Eye-plots of the posterior estimates from our full phylogeny model (including phylogenetic effects, Table ED1) include a gray distribution as the density of the posteri- ors, black circles for the median value, thick dark lines depict the 50% quantile interval, and thin black lines depict the 90% quantile interval. Letters denote groups of species — f = fish, mo = mollusc, a = arachnids, am = amphibians, b = birds, c = copeopod, d = diatom, f = fish, fu = fungi, i = insects, m = mammals, p = plants, plk = plankton, t = turtle.

Extended Data Fig. 7 Trophic level differences across our species phylogeny and well-studied evolutionary lineages.

a, Within our highly diverse and global dataset, species trophic level is highly confounded with phylogeny, limiting our ability to model trophic level directly. This is further illustrated for the four most well-sampled evolutionary lineages in our dataset, each of which map strongly to certain trophic levels, b, as all plants are primary producers, c, while most insects are primary consumers, c, the majority of birds (aves) secondary consumers, d, and all of amphibia are secondary consumers.

Extended Data Fig. 8 Species phenological shifts with temperature change across study sites.

Shifts in species phenology show no strong relationships to the rate of temperature change at each site of observation across a & e, North America (n = 42 sites), b & f, Europe (n = 109 sites), c & g, the United Kingdom (n = 98 sites), d & h, or independent studies in the United Kingdom, not including data from RIS or the Woodland trust (n sites = 14). Analyses were replicated for both the three month period around which an event occurred (a–d) and for the annual monthly temperatures (e–h). Gray bands represent the 50% quantile interval and crosses the 50%.

Extended Data Table 1 Summary of model outputs from our phylogenetic model, latitudinal model, and models of well-sample evolutionary lineages
Full size table
Extended Data Table 2 Bibliographic information of recent studies added to the database for 2015 to 2020
Full size table

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loughnan, D., Joly, S., Legault, G. et al. Phenology varies with phylogeny but not by trophic level with climate change. Nat Ecol Evol 8, 1889–1896 (2024). https://doi.org/10.1038/s41559-024-02499-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-024-02499-1

Search

Advanced search

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