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.

  • Letter
  • Published:

Changes in large-scale climate alter spatial synchrony of aphid pests

Abstract

Spatial synchrony, the tendency of distant populations to fluctuate similarly, is a major concern in ecology1,2,3,4,5,6,7,8. Except in special circumstances3,9, researchers historically had difficulty identifying drivers of synchrony in field systems5,6,10. Perhaps for this reason, the possibility9,11,12 that changes in large-scale climatic drivers may modify synchrony, thereby impacting ecosystems and human concerns, has been little examined. Here, we use wavelets to determine environmental drivers of phenological synchrony across Britain for 20 aphid species, most major crop pests. Consistently across species, changes in drivers produced large changes in aphid synchrony. Different drivers acted on different timescales: using a new wavelet analogue of the Moran theorem1, we show that on long timescales (>4 years), 80% of synchrony in aphid first flights is due to synchrony in winter climate; but this explanation accounts for less short-timescale (≤4 years) synchrony. Changes in aphid synchrony over time also differed by timescale: long-timescale synchrony fell from before 1993 to after, caused by similar changes in winter climate; whereas short-timescale synchrony increased. Shifts in winter climate are attributable to the North Atlantic Oscillation, an important climatic phenomenon7,11,13, so effects described here may influence other taxa. This study documents a new way that climatic changes influence populations, through altered Moran effects.

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

Access options

Buy this article

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

Figure 1: Synchrony of aphid first flights changed, driven by synchrony in winter climate.
Figure 2: Inter-species variation and changes in synchrony are explained by winter climate.
Figure 3: Changes in winter-temperature synchrony mirrored changes in the NAO.

Similar content being viewed by others

References

  1. Moran, P. The statistical analysis of the Canadian lynx cycle, II. Aust. J. Zool. 1, 291–298 (1953).

    Article  Google Scholar 

  2. Hanski, I. & Woiwod, I. Spatial synchrony in the dynamics of moth and aphid populations. J. Anim. Ecol. 62, 656–668 (1993).

    Article  Google Scholar 

  3. Grenfell, B. et al. Noise and determinism in synchronized sheep dynamics. Nature 394, 674–677 (1998).

    Article  CAS  Google Scholar 

  4. Lande, R., Engen, S. & Sæther, B. Spatial scale of population synchrony: Environmental correlation versus dispersal and density regulation. Am. Nat. 154, 271–281 (1999).

    Article  Google Scholar 

  5. Bjørnstad, O., Ims, R. & Lambin, X. Spatial population dynamics: Analyzing patterns and processes of population synchrony. Trends Ecol. Evol. 14, 427–432 (1999).

    Article  Google Scholar 

  6. Liebhold, A., Koenig, W. & Bjørnstad, O. Spatial synchrony in population dynamics. Annu. Rev. Ecol. Evol. Syst. 35, 467–490 (2004).

    Article  Google Scholar 

  7. Engen, S., Lande, R., Sæther, B. & Bregnballe, T. Estimating the pattern of synchrony in fluctuating populations. J. Anim. Ecol. 74, 601–611 (2005).

    Article  Google Scholar 

  8. Vasseur, D. & Fox, J. Phase-locking and environmental fluctuations generate synchrony in a predator–prey community. Nature 460, 1007–1010 (2009).

    Article  CAS  Google Scholar 

  9. Post, E. & Forchhammmer, M. Synchronization of animal population dynamics by large-scale climate. Nature 420, 168–171 (2002).

    Article  CAS  Google Scholar 

  10. Abbott, K. Does the pattern of population synchrony through space reveal if the Moran effect is acting? Oikos 116, 903–912 (2007).

    Article  Google Scholar 

  11. Stenseth, N. et al. Ecological effects of climate fluctuations. Science 297, 1292–1296 (2002).

    Article  CAS  Google Scholar 

  12. Ojanen, S., Niemenen, M., Meyke, E., Pöyry, J. & Hanski, I. Long-term metapopulation study of the Glanville fritillary butterfly (Melitaea cinxia): Survey methods, data management, and long-term population trends. Ecol. Evol. 3, 3713–3737 (2013).

    Article  Google Scholar 

  13. Ottersen, G. et al. Ecological effects of the North Atlantic Oscillation. Oecologia 128, 1–14 (2001).

    Article  Google Scholar 

  14. Bjørnstad, O., Peltonen, M., Liebhold, A. & Baltensweiler, W. Waves of larch budmoth outbreaks in the European alps. Science 298, 1020–1023 (2002).

    Article  Google Scholar 

  15. Beaugrand, G., Brander, K., Lindley, J., Souissi & Reid, P. Plankton effect on cod recruitment in the North Sea. Nature 426, 661–664 (2003).

    Article  CAS  Google Scholar 

  16. Stenseth, N. et al. The effect of climatic forcing on population synchrony and genetic structuring of the Canadian lynx. Proc. Natl Acad. Sci. USA 101, 6056–6061 (2004).

    Article  CAS  Google Scholar 

  17. Haynes, K., Bjørnstad, O., Allstadt, A. & Liebhold, A. Geographic variation in the spatial synchrony of a forest-defoliating insect: Isolation of environmental and spatial drivers. Proc. R. Soc. B 280, 20122373 (2013).

    Article  Google Scholar 

  18. Micheli, F. The dual nature of community variability. Oikos 85, 161–169 (1999).

    Article  Google Scholar 

  19. Vasseur, D. & Gaedke, U. Spectral analysis unmasks synchronous and compensatory dynamics in plankton communities. Ecology 88, 2058–2071 (2007).

    Article  Google Scholar 

  20. Keitt, T. Coherent ecological dynamics induced by large-scale disturbance. Nature 454, 331–335 (2008).

    Article  CAS  Google Scholar 

  21. Earn, D., Levin, S. & Rohani, P. Coherence and conservation. Science 290, 1360–1364 (2000).

    Article  CAS  Google Scholar 

  22. Dixon, A. Aphid Ecology: An Optimization Approach 2nd edn (Chapman and Hall, 1998).

    Google Scholar 

  23. Van Emden, H. & Harrington, R. Aphids as Crop Pests (CAB International, 2007).

    Google Scholar 

  24. Estay, S., Lima, M. & Harrington, R. Climate mediated exogenous forcing and synchrony in populations of the oak aphid in the UK. Oikos 118, 175–182 (2009).

    Article  Google Scholar 

  25. Post, E. & Forchhammer, M. C. Spatial synchrony of local populations has increased in association with the recent northern hemisphere climate trend. Proc. Natl Acad. Sci. USA 101, 9286–9290 (2004).

    Article  CAS  Google Scholar 

  26. Sheppard, L. W., Stefanovska, A. & McClintock, P. V. E. Testing for time-localized coherence in bivariate data. Phys. Rev. E 85, 046205 (2012).

    Article  CAS  Google Scholar 

  27. Harrington, R., Bale, J. & Tatchell, G. in Insects in a Changing Environment (eds Harrington, R. & Stork, N.) 125–155 (Academic Press, 1995).

    Google Scholar 

  28. Bjørnstad, O. & Falck, W. Nonparametric spatial covariance functions: Estimation and testing. Environ. Ecol. Stat. 8, 53–70 (2001).

    Article  Google Scholar 

  29. Fontaine, C. & Gonzalez, A. Population synchrony induced by resource fluctuations and dispersal in an aquatic microcosm. Ecology 86, 1463–1471 (2005).

    Article  Google Scholar 

  30. Peltonen, M., Liebhold, A., Bjørnstad, O. & Williams, D. Spatial synchrony in forest insect outbreaks: Roles of regional stochasticity and dispersal. Ecology 83, 3120–3129 (2002).

    Article  Google Scholar 

  31. Macaulay, E., Tatchell, G. & Taylor, L. The Rothamsted Insect Survey 12-metre suction trap. Bull. Entomol. Res. 78, 121–129 (1988).

    Article  Google Scholar 

  32. Harrington, R. The Rothamsted Insect Survey strikes gold. Antenna 38, 158–166 (2014).

    Google Scholar 

  33. Hurrell, J. & National Center for Atmospheric Research Staff The Climate Data Guide: Hurrell North Atlantic Oscillation (NAO) Index (Station-based) (NCAR, accessed 15 January 2015); https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-station-based.

  34. National Center for Atmospheric Research Staff. The Climate Data Guide: Hurrell North Atlantic Oscillation (NAO) Index (PC-based) (NCAR, accessed 15 January 2015); https://climatedataguide.ucar.edu/climate-data/hurrell-north-atlantic-oscillation-nao-index-pc-based.

  35. UK Climate: Historical Station Data (UK Met Office, accessed 15 January 2015); http://www.metoffice.gov.uk/public/weather/climate-historic/#?tab=climateHistoric.

  36. Grenfell, B., Bjørnstad, O. & Kappey, J. Traveling waves and spatial hierarchies in measles epidemics. Nature 414, 716–723 (2001).

    Article  CAS  Google Scholar 

  37. Viboud, C. et al. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312, 447–451 (2006).

    Article  CAS  Google Scholar 

  38. Keitt, T. & Fischer, J. Detection of scale-specific community dynamics using wavelets. Ecology 87, 2895–2904 (2006).

    Article  Google Scholar 

  39. Cazelles, B. et al. Wavelet analysis of ecological timeseries. Oecologia 156, 287–304 (2008).

    Article  Google Scholar 

  40. Bell, J. et al. Putting the brakes on a cycle: Bottom-up effects damp cycle amplitude. Ecol. Lett. 15, 310–318 (2012).

    Article  Google Scholar 

  41. Cazelles, B., Cazelles, K. & Chavez, M. Wavelet analysis in ecology and epidemiology: Impact of statistical tests. J. R. Soc. Interface 11, 20130585 (2014).

    Article  Google Scholar 

  42. Rouyer, T., Fromentin, J., Stenseth, N. & Cazelles, B. Analysing multiple time series and extending significance testing in wavelet analysis. Mar. Ecol. Prog. Ser. 359, 11–23 (2008).

    Article  Google Scholar 

  43. Torrence, C. & Compo, G. P. A practical guide to wavelet analysis. Bull. Am. Meteorol. Soc. 79, 61–78 (1998).

    Article  Google Scholar 

  44. Addison, P. S. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance (Taylor and Francis, 2002).

    Book  Google Scholar 

  45. Meyers, S., Kelly, B. & O’Brien, J. An introduction to wavelet analysis in oceanography and meteorology: With application to the dispersion of yanai waves. Mon. Weath. Rev. 121, 2858–2866 (1993).

    Article  Google Scholar 

  46. Bandrivskyy, A., Bernjak, A., McClintock, P. V. E. & Stefanovska, A. Wavelet phase coherence analysis: Application to skin temperature and blood flow. Cardiovasc. Eng. 4, 89–93 (2004).

    Article  Google Scholar 

  47. Stefanovska, A. Coupled oscillators—complex but not complicated cardiovascular and brain interactions. IEEE Eng. Med. Biol. 26, 25–29 (2007).

    Article  Google Scholar 

  48. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B. & Farmer, J. Testing for nonlinearity in time series: The method of surrogate data. Physica D 58, 77–94 (1992).

    Article  Google Scholar 

  49. Prichard, D. & Theiler, J. Generating surrogate data for time series with several simultaneously measured variables. Phys. Rev. Lett. 73, 951–954 (1994).

    Article  CAS  Google Scholar 

  50. Jones, J., Doran, P. & Holmes, R. Climate and food synchronize regional forest bird abundances. Ecology 84, 3024–3032 (2003).

    Article  Google Scholar 

  51. Grøtan, V. et al. Climate causes large-scale spatial synchrony in population fluctuations of a temperate herbivore. Ecology 86, 1472–1482 (2005).

    Article  Google Scholar 

  52. Sæther, B. et al. The extended Moran effect and large-scale synchronous fluctuations in the size of great tit and blue tit populations. J. Anim. Ecol. 76, 315–325 (2007).

    Article  Google Scholar 

  53. Schreiber, T. & Schmitz, A. Surrogate time series. Physica D 142, 346–382 (2000).

    Article  Google Scholar 

  54. Sakia, R. The Box–Cox transformation technique: A review. Statistician 41, 169–178 (1992).

    Article  Google Scholar 

Download references

Acknowledgements

We thank contributors to the Rothamsted Insect Survey; P. Verrier for data extraction; and B. Cazelles, J. E. Cohen, R. Costantino, R. Desharnais, E. Defriez, J. Kastens, B. Mechtley and C. Reid for advice and discussions. The Rothamsted Insect Survey is a UK BBSRC-supported National Capability. L.W.S. was supported and D.C.R. was partly supported by UK NERC grants NE/H020705/1, NE/I010963/1 and NE/I011889/1 and funding from the University of Kansas. Travel was facilitated by US National Science Foundation grant DMS-1225529.

Author information

Authors and Affiliations

Authors

Contributions

L.W.S. and D.C.R. designed and carried out the analysis and wrote the paper. Data and interpretive assistance were provided by J.R.B. and R.H. All authors contributed to editing.

Corresponding authors

Correspondence to Lawrence W. Sheppard or Daniel C. Reuman.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheppard, L., Bell, J., Harrington, R. et al. Changes in large-scale climate alter spatial synchrony of aphid pests. Nature Clim Change 6, 610–613 (2016). https://doi.org/10.1038/nclimate2881

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate2881

This article is cited by

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