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Detecting climate signals cascading through levels of biological organization

Nature Climate Change volume 13, pages 985–989 (2023)Cite this article

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Abstract

Threats to species under climate change can be understood as a time at which the signal of climate change in ecological processes emerges from the noise of ecosystem variability, defined as ‘time of emergence’ (ToE). Here we show that ToE for the great tit (Parus major) will occur earlier at the level of population size than trait (laying date) and vital rates (survival, recruitment) under the RCP 8.5 scenario, suggesting an amplified climate change signal at the population level. ToE thus varies across levels of biological organization that filter trends and variability in climate differently. This has implications for the detection of climate impacts on wild species, as a shift in population size may precede changes in traits and vital rates. Further work would need to identify the ecological level that may experience an earlier detection of the climate signal for species with contrasting life histories, climate trends and variability.

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Fig. 1: Schematic illustration of the general approach.
Fig. 2: Caterpillar peak dates, great tit laying dates and their mismatch forecasted under global warming in the studied great tit population between 1920 and 2100.
Fig. 3: ToE from caterpillar peak dates to population size in the Hoge Veluwe great tit population.

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Data availability

Data used in the analysis are available at https://github.com/marleng/ToE_greattit. Data on past observed beech crop index, past observed mismatch between laying dates and food peaks, expected spring temperature according to the RCP 8.5 scenario, past observed annual age-specific population size and vital rates and their variance are provided.

Code availability

R code used for the analysis is available at https://github.com/marleng/ToE_greattit.

References

  1. Jenouvrier, S. et al. Detecting climate signals in populations across life histories. Glob. Change Biol. 28, 2236–2258 (2022).

    CAS  Google Scholar 

  2. Malhi, Y. et al. Climate change and ecosystems: threats, opportunities and solutions. Phil. Trans. R. Soc. B 375, 20190104 (2020).

    CAS  Google Scholar 

  3. Mahlstein, I., Knutti, R., Solomon, S. & Portmann, R. W. Early onset of significant local warming in low latitude countries. Environ. Res. Lett. 6, 034009 (2011).

    Google Scholar 

  4. Landrum, L. & Holland, M. M. Extremes become routine in an emerging new Arctic. Nat. Clim. Change 10, 1108–1115 (2020).

    Google Scholar 

  5. Rojas, M., Lambert, F., Ramirez-Villegas, J. & Challinor, A. J. Emergence of robust precipitation changes across crop production areas in the 21st century. Proc. Natl Acad. Sci. USA 116, 6673–6678 (2019).

    CAS  Google Scholar 

  6. Clements, C. F., Blanchard, J. L., Nash, K. L., Hindell, M. A. & Ozgul, A. Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat. Ecol. Evol. 1, 0188 (2017).

    Google Scholar 

  7. Clements, C. F. & Ozgul, A. Including trait-based early warning signals helps predict population collapse. Nat. Commun. 7, 10984 (2016).

    CAS  Google Scholar 

  8. Baruah, G., Clements, C. F., Guillaume, F. & Ozgul, A. When do shifts in trait dynamics precede population declines? Am. Nat. 193, 633–644 (2019).

    Google Scholar 

  9. Hilde, C. H. et al. The demographic buffering hypothesis: evidence and challenges. Trends Ecol. Evol. 35, 523–538 (2020).

    Google Scholar 

  10. Visser, M. E., van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. B 265, 1867–1870 (1998).

    Google Scholar 

  11. Reed, T. E., Jenouvrier, S. & Visser, M. E. Phenological mismatch strongly affects individual fitness but not population demography in a woodland passerine. J. Anim. Ecol. 82, 131–144 (2013).

    Google Scholar 

  12. Övergaard, R., Gemmel, P. & Karlsson, M. Effects of weather conditions on mast year frequency in beech (Fagus sylvatica L.) in Sweden. Int. J. For. Res. 80, 555–565 (2007).

    Google Scholar 

  13. Nussbaumer, A. et al. Patterns of mast fruiting of common beech, sessile and common oak, Norway spruce and Scots pine in Central and Northern Europe. For. Ecol. Manag. 363, 237–251 (2016).

    Google Scholar 

  14. Perdeck, A. C., Visser, M. E. & Van Balen, J. H. Great tit Parus major survival and the beech-crop cycle. Ardea 88, 99–106 (2000).

    Google Scholar 

  15. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Google Scholar 

  16. 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, 20211337 (2021).

    Google Scholar 

  17. Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).

    CAS  Google Scholar 

  18. Porlier, M. et al. Variation in phenotypic plasticity and selection patterns in blue tit breeding time: between- and within-population comparisons. J. Anim. Ecol. 81, 1041–1051 (2012).

    Google Scholar 

  19. Gamelon, M. et al. Environmental drivers of varying selective optima in a small passerine: a multivariate, multiepisodic approach. Evolution 72, 2325–2342 (2018).

    Google Scholar 

  20. Marrot, P., Charmantier, A., Blondel, J. & Garant, D. Current spring warming as a driver of selection on reproductive timing in a wild passerine. J. Anim. Ecol. 87, 754–764 (2018).

    Google Scholar 

  21. Le Vaillant, J., Potti, J., Camacho, C., Canal, D. & Martínez-Padilla, J. Fluctuating selection driven by global and local climatic conditions leads to stasis in breeding time in a migratory bird. J. Evol. Biol. 34, 1541–1553 (2021).

    Google Scholar 

  22. Vatka, E., Orell, M., Rytkönen, S. & Merilä, J. Effects of ambient temperatures on evolutionary potential of reproductive timing in boreal passerines. J. Anim. Ecol. 90, 367–375 (2021).

    Google Scholar 

  23. Sæther, B.-E., Engen, S., Gustafsson, L., Grøtan, V. & Vriend, S. J. G. Density-dependent adaptive topography in a small passerine bird, the collared flycatcher. Am. Nat. 197, 93–110 (2021).

    Google Scholar 

  24. Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–804 (2008).

    CAS  Google Scholar 

  25. Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).

    Google Scholar 

  26. Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).

    Google Scholar 

  27. de Villemereuil, P. et al. Fluctuating optimum and temporally variable selection on breeding date in birds and mammals. Proc. Natl Acad. Sci. USA 117, 31969–31978 (2020).

    Google Scholar 

  28. Besbeas, P., Freeman, S. N., Morgan, B. J. T. & Catchpole, E. A. Integrating mark–recapture–recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58, 540–547 (2002).

    CAS  Google Scholar 

  29. Schaub, M. & Abadi, F. Integrated population models: a novel analysis framework for deeper insights into population dynamics. J. Ornithol. 152, 227–237 (2011).

    Google Scholar 

  30. Brooks, S. P., King, R. & Morgan, B. J. T. A Bayesian approach to combining animal abundance and demographic data. Anim. Biodivers. Conserv. 27, 515–529 (2004).

    Google Scholar 

  31. Schaub, M. & Kéry, M. Integrated Population Models: Theory and Ecological Applications with R and JAGS (Elsevier Science, 2021).

  32. Drobyshev, I., Niklasson, M., Mazerolle, M. J. & Bergeron, Y. Reconstruction of a 253-year long mast record of European beech reveals its association with large scale temperature variability and no long-term trend in mast frequencies. Agric. For. Meteorol. 192–193, 9–17 (2014).

    Google Scholar 

  33. Bogdziewicz, M., Kelly, D., Thomas, P. A., Lageard, J. G. A. & Hacket-Pain, A. Climate warming disrupts mast seeding and its fitness benefits in European beech. Nat. Plants 6, 88–94 (2020).

    Google Scholar 

  34. Bogdziewicz, M. et al. Climate warming causes mast seeding to break down by reducing sensitivity to weather cues. Glob. Change Biol. 27, 1952–1961 (2021).

    CAS  Google Scholar 

  35. Reed, T. E., Grøtan, V., Jenouvrier, S., Sæther, B.-E. & Visser, M. E. Population growth in a wild bird is buffered against phenological mismatch. Science 340, 488–491 (2013).

    CAS  Google Scholar 

  36. Beaumont, L. J. et al. Impacts of climate change on the world’s most exceptional ecoregions. Proc. Natl Acad. Sci. USA 108, 2306–2311 (2011).

    CAS  Google Scholar 

  37. Hawkins, E. et al. Observed emergence of the climate change signal: from the familiar to the unknown. Geophys. Res. Lett. 47, e2019GL086259 (2020).

  38. Husby, A., Kruuk, L. E. B. & Visser, M. E. Decline in the frequency and benefits of multiple brooding in great tits as a consequence of a changing environment. Proc. R. Soc. B 276, 1845–1854 (2009).

    Google Scholar 

  39. Grøtan, V. et al. Spatial and temporal variation in the relative contribution of density dependence, climate variation and migration to fluctuations in the size of great tit populations. J. Anim. Ecol. 78, 447–459 (2009).

    Google Scholar 

  40. Dhondt, A. A., Adriaensen, F., Matthysen, E. & Kempenaers, B. Nonadaptive clutch sizes in tits. Nature 348, 723–725 (1990).

    Google Scholar 

  41. Ramakers, J. J. C., Gienapp, P. & Visser, M. E. Comparing two measures of phenological synchrony in a predator–prey interaction: simpler works better. J. Anim. Ecol. 89, 745–756 (2020).

    Google Scholar 

  42. Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).

    CAS  Google Scholar 

  43. Kéry, M. & Schaub, M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective (Academic Press, 2012).

  44. Lebreton, J.-D. & Gimenez, O. Detecting and estimating density dependence in wildlife populations. J. Wildl. Manage. 77, 12–23 (2013).

    Google Scholar 

  45. Lande, R. et al. Estimating density dependence from population time series using demographic theory and life-history data. Am. Nat. 159, 321–337 (2002).

    CAS  Google Scholar 

  46. Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).

    Google Scholar 

  47. de Valpine, P. & Hastings, A. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72, 57–76 (2002).

    Google Scholar 

  48. de Valpine, P. et al. Programming with models: writing statistical algorithms for general model structures with NIMBLE. J. Comput. Graph. Stat. 26, 403–413 (2017).

    Google Scholar 

  49. Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455 (1998).

    Google Scholar 

  50. Gamelon, M. et al. Density dependence in an age-structured population of great tits: identifying the critical age classes. Ecology 97, 2479–2490 (2016).

    Google Scholar 

  51. Hansen, B. B. et al. More frequent extreme climate events stabilize reindeer population dynamics. Nat. Commun. 10, 1616 (2019).

    Google Scholar 

  52. Abadi, F. et al. Estimating the strength of density dependence in the presence of observation errors using integrated population models. Ecol. Modell. 242, 1–9 (2012).

    Google Scholar 

  53. Gamelon, M. et al. Interactions between demography and environmental effects are important determinants of population dynamics. Sci. Adv. 3, e1602298 (2017).

    Google Scholar 

  54. Freckleton, R. P., Watkinson, A. R., Green, R. E. & Sutherland, W. J. Census error and the detection of density dependence. J. Anim. Ecol. 75, 837–851 (2006).

    Google Scholar 

  55. Schaub, M., Jakober, H. & Stauber, W. Strong contribution of immigration to local population regulation: evidence from a migratory passerine. Ecology 94, 1828–1838 (2013).

    Google Scholar 

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

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Acknowledgements

We are grateful to all those who have collected data and to L. Vernooij and J. Risse for maintaining the long-term great tit database. We thank the board of the National Park of Hoge Veluwe for permission to carry out our research in their park. This work was supported by the Research Council of Norway through its Centres of Excellence funding scheme, project number 223257, by NASA grant number 80NSSC20K1289 to S.J. and by the CNRS.

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Authors and Affiliations

  1. Laboratoire de Biométrie et Biologie Evolutive, UMR 5558 CNRS, Université Claude Bernard Lyon 1, Villeurbanne, France

    Marlène Gamelon

  2. Department of Biology, Centre for Biodiversity Dynamics, Norwegian University of Science and Technology, Trondheim, Norway

    Marlène Gamelon & Bernt-Erik Sæther

  3. Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Stéphanie Jenouvrier

  4. Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, the Netherlands

    Melanie Lindner & Marcel E. Visser

  5. Chronobiology Unit, Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, the Netherlands

    Melanie Lindner & Marcel E. Visser

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  1. Marlène Gamelon
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Contributions

All the authors conceived the research idea. M.G. and S.J. designed the methodology and carried out the formal analysis. M.E.V. undertook data acquisition and M.E.V. and B.-E.S. funding acquisition. M.G. wrote the original draft and created the visuals and all authors were involved with reviewing and editing the manuscript.

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Correspondence to Marlène Gamelon.

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Nature Climate Change thanks Qing Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Forecast of food peak dates, laying dates and mismatch under the RCP 8.5 scenario in the studied population between 1920 and 2100, when all sources of uncertainties are accounted for in the projections.

Black line corresponds to the median, shaded dark gray corresponds to 66% and light gray to 95% prediction intervals. Vertical dotted lines indicate the historical period (1922–1950), horizontal line indicates the lower bound of the 66% interval during that period for food peak dates and laying dates and indicates the upper bound of the interval for the mismatch. Vertical red lines correspond to the time of emergence (ToE). In yellow, annual observed values between 1985 and 2020 are provided.

Extended Data Fig. 2 Directed acyclic graph of the integrated population model (IPM). Squares represent the data, circles represent the parameters to be estimated.

Arrows represent dependencies. Three types of demographic data are collected: CMR data (m), count data (C) and fecundity data (number of daughters locally recruited (J) and number of mothers (B) in each age class i at year t). Parameters estimated with the IPM are the annual recapture probability pt, annual age-specific survival Si,t, annual age-specific recruitment Ri,t, annual number of immigrants Nimt and annual population size Nt.

Extended Data Fig. 3 Two extreme scenarios of beech crop production (scenarios 1 and 2).

Displayed are the probabilities of having a year of high BCI (level 3)). Black lines correspond to the median, shaded dark gray corresponds to 66% and light gray to 95% prediction intervals.

Extended Data Fig. 4 Age-specific vital rates, number of immigrants and population size forecasted in the studied great tit population between 1920 and 2100, when climate uncertainty is accounted for in the projections, under a scenario of decreasing beech crop production (scenario 1).

Each line corresponds to one climate scenario (40 in total), and the black lines correspond to the mean. Vertical dotted lines indicate the historical period (1922–1950), horizontal lines indicate the lower bound of the 66% interval during that period. Vertical red lines correspond to the time of emergence (ToE).

Extended Data Fig. 5 Age-specific vital rates, number of immigrants and population size forecasted in the studied great tit population between 1920 and 2100, when all sources of ecological uncertainties are accounted for in the projections, under a scenario of decreasing beech crop production (scenario 1).

Black lines correspond to the median, shaded dark gray corresponds to 66% and light gray to 95% prediction intervals. Vertical dotted lines indicate the historical period (1922–1950), horizontal line indicates the lower bound of the 66% interval during that period. Vertical red line corresponds to the time of emergence (ToE).

Extended Data Fig. 6 Age-specific vital rates, number of immigrants and population size forecasted in the studied great tit population between 1920 and 2100, when climate uncertainty is accounted for in the projections, under a scenario of increasing beech crop production (scenario 2).

Each line corresponds to one climate scenario (40 in total), and the black lines correspond to the mean. Vertical dotted lines indicate the historical period (1922–1950), horizontal lines indicate the upper bound of the 66% interval during that period. Vertical red lines correspond to the time of emergence (ToE).

Extended Data Fig. 7 Age-specific vital rates, number of immigrants and population size forecasted in the studied great tit population between 1920 and 2100, when all sources of ecological uncertainties are accounted for in the projections, under a scenario of increasing beech crop production (scenario 2).

Black lines correspond to the median, shaded dark gray corresponds to 66% and light gray to 95% prediction intervals. Vertical dotted lines indicate the historical period (1922–1950), horizontal line indicates the upper bound of the 66% interval during that period. Vertical red line corresponds to the time of emergence (ToE).

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Gamelon, M., Jenouvrier, S., Lindner, M. et al. Detecting climate signals cascading through levels of biological organization. Nat. Clim. Chang. 13, 985–989 (2023). https://doi.org/10.1038/s41558-023-01760-y

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