Abstract
Differences in phenological responses to climate change among species can desynchronise ecological interactions and thereby threaten ecosystem function. To assess these threats, we must quantify the relative impact of climate change on species at different trophic levels. Here, we apply a Climate Sensitivity Profile approach to 10,003 terrestrial and aquatic phenological data sets, spatially matched to temperature and precipitation data, to quantify variation in climate sensitivity. The direction, magnitude and timing of climate sensitivity varied markedly among organisms within taxonomic and trophic groups. Despite this variability, we detected systematic variation in the direction and magnitude of phenological climate sensitivity. Secondary consumers showed consistently lower climate sensitivity than other groups. We used mid-century climate change projections to estimate that the timing of phenological events could change more for primary consumers than for species in other trophic levels (6.2 versus 2.5–2.9 days earlier on average), with substantial taxonomic variation (1.1–14.8 days earlier on average).
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References
IPCC. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 1132 (Cambridge Univ. Press, 2014)
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003)
Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003)
Both, C., van Asch, M., Bijlsma, R. G., van den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J. Anim. Ecol. 78, 73–83 (2009)
Visser, M. E., Holleman, L. J. M. & Gienapp, P. Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147, 164–172 (2006).
Burthe, S. et al. Phenological trends and trophic mismatch across multiple levels of a North Sea pelagic food web. Mar. Ecol. Prog. Ser. 454, 119–133 (2012)
Jonsson, T. & Setzer, M. A freshwater predator hit twice by the effects of warming across trophic levels. Nat. Commun. 6, 5992 (2015)
Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313 (2010)
Visser, M. E. & Both, C. Shifts in phenology due to global climate change: the need for a yardstick. Proc. R. Soc. Lond. B 272, 2561–2569 (2005)
Walpole, M. et al. Ecology. Tracking progress toward the 2010 biodiversity target and beyond. Science 325, 1503–1504 (2009)
Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010)
Williams, S. E., Shoo, L. P., Isaac, J. L., Hoffmann, A. A. & Langham, G. Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biol. 6, e325 (2008)
Post, E. & Forchhammer, M. C. Climate change reduces reproductive success of an Arctic herbivore through trophic mismatch. Philos. Trans. R. Soc. B Biol. Sci. 363, 2367–2373 (2008)
Thackeray, S. J., Jones, I. D. & Maberly, S. C. Long-term change in the phenology of spring phytoplankton: species-specific responses to nutrient enrichment and climatic change. J. Ecol. 96, 523–535 (2008)
Doi, H., Gordo, O. & Katano, I. Heterogeneous intra-annual climatic changes drive different phenological responses at two trophic levels. Clim. Res. 36, 181–190 (2008)
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. Lond. B 265, 1867–1870 (1998)
van de Pol, M. & Cockburn, A. Identifying the critical climatic time window that affects trait expression. Am. Nat. 177, 698–707 (2011)
Ohlberger, J., Thackeray, S., Winfield, I., Maberly, S. & Vøllestad, L. When phenology matters: age–size truncation alters population response to trophic mismatch. Proc. R. Soc. Lond. B 281, 20140938 (2014)
Thackeray, S. J., Henrys, P. A., Jones, I. D. & Feuchtmayr, H. Eight decades of phenological change for a freshwater cladoceran: what are the consequences of our definition of seasonal timing? Freshw. Biol. 57, 345–359 (2012)
Phillimore, A. B., Hadfield, J. D., Jones, O. R. & Smithers, R. J. Differences in spawning date between populations of common frog reveal local adaptation. Proc. Natl Acad. Sci. USA 107, 8292–8297 (2010); correction 109, 5134 (2012)
Amano, T., Smithers, R. J., Sparks, T. H. & Sutherland, W. J. A 250-year index of first flowering dates and its response to temperature changes. Proc. R. Soc. Lond. B 277, 2451–2457 (2010)
Feuchtmayr, H. et al. Spring phytoplankton phenology — are patterns and drivers of change consistent among lakes in the same climatological region? Freshw. Biol. 57, 331–344 (2012)
Nussey, D. H., Clutton-Brock, T. H., Albon, S. D., Pemberton, J. & Kruuk, L. E. B. Constraints on plastic responses to climate variation in red deer. Biol. Lett. 1, 457–460 (2005)
Van Emden, H. F. & Harrington, R. Aphids as Crop Pests. 717 (CABI, 2007)
Cook, B. I., Wolkovich, E. M. & Parmesan, C. Divergent responses to spring and winter warming drive community level flowering trends. Proc. Natl Acad. Sci. USA 109, 9000–9005 (2012)
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)
Amano, T. et al. Links between plant species’ spatial and temporal responses to a warming climate. Proc. R. Soc. Lond. B 281, 20133017 (2014)
Miller-Rushing, A. J., Hoye, T. T., Inouye, D. W. & Post, E. The effects of phenological mismatches on demography. Philos. Trans. R. Soc. B 365, 3177–3186 (2010)
Nakazawa, T. & Doi, H. A perspective on match/mismatch of phenology in community contexts. Oikos 121, 489–495 (2012)
Scheffer, M. et al. Creating a safe operating space for iconic ecosystems. Science 347, 1317–1319 (2015)
Perry, M. & Hollis, D. The generation of monthly gridded data sets for a range of climatic variables over the UK. Int. J. Climatol. 25, 1041–1054 (2005)
Jones, I. D., Winfield, I. J. & Carse, F. Assessment of long-term changes in habitat availability for Arctic charr (Salvelinus alpinus) in a temperate lake using oxygen profiles and hydroacoustic surveys. Freshw. Biol. 53, 393–402 (2008)
Elliott, J. M. Numerical changes and population regulation in young migratory trout Salmo trutta in a Lake District stream, 1966–83. J. Anim. Ecol. 53, 327–350 (1984)
Mohseni, O., Stefan, H. G. & Erickson, T. R. A nonlinear regression model for weekly stream temperatures. Wat. Resour. Res. 34, 2685–2692 (1998)
Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003)
Reid, P. C., Colebrook, J. M., Matthews, J. B. L. & Aiken, J. The Continuous Plankton Recorder: concepts and history, from Plankton Indicator to undulating recorders. Prog. Oceanogr. 58, 117–173 (2003)
Pope, K. S. et al. Detecting nonlinear response of spring phenology to climate change by Bayesian analysis. Glob. Change Biol. 19, 1518–1525 (2013)
R Development Core Team. R: A Language and Environment for Statistical Computing (2011)
Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004)
Bates, D., Maechler, M. & Bolker, B. lme4: Linear Mixed-Effects Models using S4 Classes (2011)
Acknowledgements
This work was funded by Natural Environment Research Council (NERC) grant NE/J02080X/1. We thank O. Mountford for assigning species traits for plants, H. Feuchtmayr for extracting plankton data for analysis and N. Dodd for air and water temperature data from the Tarland Burn. We also thank P. Verrier, the staff and many volunteers and contributors, including Science and Advice for Scottish Agriculture, to the Rothamsted Insect Survey (RIS) over the last half century. The RIS is a National Capability strategically funded by BBSRC. The consortium represented by the authorship list hold long-term data that represent a considerable investment in scientific endeavour. Whilst we are committed to sharing these data for scientific research, users are requested to collaborate before publication of these data to ensure accurate biological interpretation.
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S.J.T. and S.W. conceived and coordinated the study and led writing of the manuscript. P.A.H. developed the analysis routine and wrote statistical code to be applied to all data sets. D.H. extracted all climatic and sea surface temperature data. I.D.J. and E.B.M. calculated water temperatures for lakes and streams, respectively. S.J.T., J.R.B., M.S.B., S.B., P.H., T.T.H., D.G.J., D.I.L., E.B.M. and D.M. led analysis of specific data sets using code from P.A.H. S.A., P.J.B., T.M.B., L.C., T.H.C.-B., C.D., M.E., J.M.E., S.J.G.H., R.H., J.W.P.-H., L.E.B.K., J.M.P., T.H.S., P.M.T., I.W. and I.J.W. derived phenological data for analysis, advised on interpretation, and assisted in assigning species traits. All co-authors commented on the manuscript.
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Reviewer Information Nature thanks D. Inouye, M. Visser and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Limits of phenological temperature sensitivity inclusive of marine plankton data.
a–c, Upper and lower limits of phenological temperature sensitivity are quantified as the slope of the relationship between seasonal timing (day of year) and temperature (°C) variation within specific seasonal periods. Limits in temperature sensitivity are shown for all taxa (a) and by trophic level (lower limit, b; upper limit, c). Inverted triangles indicate average sensitivity for all species in each group and curves are probability density plots of species-level variation in sensitivity (n = 379,081).
Extended Data Figure 2 Limits of phenological climate sensitivity for taxonomic groups (top) and trophic levels (bottom), after Monte-Carlo resampling.
a, b, Lower (blue) and upper (red) limits of the sensitivity of phenological events to changes in seasonal temperature (a) and precipitation (b). Coloured circles: responses based upon the full data set. Bars: 2.5th–97.5th percentile responses for each group, based upon 100 draws from the full data set. Data were sampled so that 5, (dotted bar), 20 (solid bar), 50 (dashed bar) and 100 (dot-dashed bar) phenological time series were drawn from each taxonomic group (n = 370,725).
Extended Data Figure 3 Climate sensitivities, based on different time periods.
Top: all data; middle: pre-1980 data; bottom: post-1980 data. Sensitivity is the slope of the relationship between seasonal timing (day of year) and temperature (°C) or precipitation (mm per day). a, b, Limits of temperature (a) and precipitation (b) sensitivity are summarized for all taxa. c–f, Lower (c, d) and upper (e, f) limits of temperature (c, e) and precipitation (d, f) sensitivity are shown by trophic level. Inverted triangles: average sensitivity for all species (a, b) or trophic levels (c–f). Curves, kernel density plots: probability density distributions of species-level climate sensitivity (that is, the relative likelihood of different climate sensitivities within each species group) (n = 370,725).
Extended Data Figure 4 Limits of phenological climate sensitivity for broad taxonomic groups.
Top, all data; bottom, post-1980 data only. a, b, Lower (blue) and upper (red) limits of the sensitivity of phenological events to seasonal temperature (a) and precipitation (b) change are shown. Coloured circles indicate the median response, and bars show the 5th–95th percentile responses for each group. Sensitivity is quantified by summarizing the species-level (random effects) responses from a mixed effects model including data for all taxa, and with taxonomic group as a fixed effect (n = 370,725).
Extended Data Figure 5 Seasonal windows for CSPs.
a–d, Estimated climatic sensitivity at the lower (a, c) and upper (b, d) limits of CSPs for 10,003 phenological series. Grey lines are seasonal time periods (x-axis) within which climatic variables have their most positive or negative correlations with the seasonal timing of each phenological event. The y-axis indicates the slope coefficient for each of these correlations; a measure of climate sensitivity (days change per °C or per mm). Shown are the lower and upper limits of CSPtemp (a and b, respectively) and the lower and upper limits of CSPprecip (c and d, respectively). Inset histograms show seasonal time window length (days) (n = 370,725).
Extended Data Figure 6 Time lags between phenological events and seasonal windows of climate sensitivity.
a–d, Frequency histograms showing the time lag (in days) between the mean timing of each phenological event and the end of seasonal windows corresponding to the lower and upper limits of CSPtemp (a and b, respectively) and the lower and upper limits of CSPprecip (c and d, respectively). Peaks at lags of around 1 year are where windows were identified that ended at the mean seasonal timing of an event, but in the previous year, owing to temporal autocorrelation in climate data (n = 370,725).
Extended Data Figure 7 Seasonal windows for CSPs by trophic level.
Estimated climatic sensitivity at the lower and upper limits of CSPs for taxa at each of three trophic levels. Formatting is as in Extended Data Fig. 5. a–d, Lower and upper limits of CSPtemp (a and b, respectively) and the lower and upper limits of CSPprecip (c and d, respectively) (n = 370,725).
Extended Data Figure 8 Example CSP.
Temperature sensitivity (CSPtemp) for alderfly (Sialis lutaria) emergence from Windermere, UK. Solid black line: sensitivity of first emergence to water temperature on different days of the year (days change per °C). Grey horizontal lines: 2.5th and 97.5th percentiles of these sensitivity values. Solid orange curve: GAM smoother fitted through the sensitivity values with associated confidence intervals (dashed orange curves). Horizontal bars indicate where GAM confidence intervals exceed the percentiles of the original sensitivity values, indicating seasonal windows at the limits of the climate sensitivity profile (n = 30).
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This file contains a Supplementary Discussion, a schematic overview of the analytical approach and Supplementary Tables 1-2. (PDF 369 kb)
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Thackeray, S., Henrys, P., Hemming, D. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016). https://doi.org/10.1038/nature18608
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DOI: https://doi.org/10.1038/nature18608
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