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Mast seeding patterns are asynchronous at a continental scale


Resource pulses are rare events with a short duration and high magnitude that drive the dynamics of both plant and animal populations and communities1. Mast seeding is perhaps the most common type of resource pulse that occurs in terrestrial ecosystems2, is characterized by the synchronous and highly variable production of seed crops by a population of perennial plants3,4, is widespread both taxonomically and geographically5, and is often associated with nutrient scarcity6. The rare production of abundant seed crops (mast events) that are orders of magnitude greater than crops during low seed years leads to high reproductive success in seed consumers and has cascading impacts in ecosystems2,7. Although it has been suggested that mast seeding is potentially synchronized at continental scales8, studies are largely constrained to local areas covering tens to hundreds of kilometres. Furthermore, summer temperature, which acts as a cue for mast seeding9, shows patterns at continental scales manifested as a juxtaposition of positive and negative anomalies that have been linked to irruptive movements of boreal seed-eating birds10,11. Here, we show a breakdown in synchrony of mast seeding patterns across space, leading to asynchrony at the continental scale. In an analysis of synchrony for a transcontinental North America tree species spanning distances of greater than 5,200 km, we found that mast seeding patterns were significantly asynchronous at distances of greater than 2,000 km apart (all P < 0.05). Other studies have shown declines in synchrony across distance, but not asynchrony. Spatiotemporal variation in summer temperatures at the continental scale drives patterns of synchrony in mast seeding, and we anticipate that this affects the spatial dynamics of numerous seed-eating communities, from insects to small mammals to the large-scale migration patterns of boreal seed-eating birds.

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Fig. 1: Correlograms for mast seeding and weather patterns between sites up to distances of 5,227 km apart.
Fig. 2: Mast seeding patterns for white spruce at sites in Yukon and Quebec.
Fig. 3: Maps of ∆T of mean July temperature and mast-event occurrence.

Data availability

The data that support the findings of this study are available in Dryad at


  1. 1.

    Yang, L. H., Bastow, J. L., Spence, K. O. & Wright, A. N. What can we learn from resource pulses? Ecology 89, 621–634 (2008).

    Article  Google Scholar 

  2. 2.

    Ostfeld, R. S. & Keesing, F. Pulsed resources and community dynamics of consumers in terrestrial ecosystems. Trends Ecol. Evol. 15, 232–237 (2000).

    CAS  Article  Google Scholar 

  3. 3.

    Kelly, D. The evolutionary ecology of mast seeding. Trends Ecol. Evol. 9, 465–470 (1994).

    CAS  Article  Google Scholar 

  4. 4.

    Silvertown, J. W. The evolutionary ecology of mast seeding in trees. Biol. J. Linn. Soc. 14, 235–250 (1980).

    Article  Google Scholar 

  5. 5.

    Pearse, I. S., LaMontagne, J. M. & Koenig, W. D. Inter-annual variation in seed production has increased over time (1900–2014). Proc. R. Soc. B 284, 20171666 (2017).

  6. 6.

    Fernández-Martínez, M. et al. Nutrient scarcity as a selective pressure for mast seeding. Nat. Plants 5, 1222–1228 (2019).

    Article  Google Scholar 

  7. 7.

    Kelly, D., Koenig, W. D. & Liebhold, A. M. An intercontinental comparison of the dynamic behavior of mast seeding communities. Popul. Ecol. 50, 329–342 (2008).

    Article  Google Scholar 

  8. 8.

    Koenig, W. D. & Knops, J. M. H. Scale of mast-seeding and tree-ring growth. Nature 396, 225–226 (1998).

    CAS  Article  Google Scholar 

  9. 9.

    Krebs, C. J., LaMontagne, J. M., Kenney, A. J. & Boutin, S. Climatic determinants of white spruce cone crops in the boreal forest of southwestern Yukon. Botany 90, 113–119 (2012).

    Article  Google Scholar 

  10. 10.

    Strong, C., Zuckerberg, B., Betancourt, J. L. & Koenig, W. D. Climatic dipoles drive two principal modes of North American boreal bird irruption. Proc. Natl Acad. Sci. USA 112, 2795–2802 (2015).

    Article  Google Scholar 

  11. 11.

    Zuckerberg, B. et al. Climate dipoles as continental drivers of plant and animal populations. Trends Ecol. Evol. 35, 440–453 (2020).

    Article  Google Scholar 

  12. 12.

    Mooney, K. A., Linhart, Y. B. & Snyder, M. A. Masting in ponderosa pine: comparisons of pollen and seed over space and time. Oecologia 165, 651–661 (2011).

    Article  Google Scholar 

  13. 13.

    Norton, D. A. & Kelly, D. Mast seeding over 33 years by Dacrydium cupressinum Lamb. (rimu) (Podocarpaceae) in New Zealand: the importance of economies of scale. Funct. Ecol. 2, 399–408 (1988).

    Article  Google Scholar 

  14. 14.

    Koenig, W. D. & Knops, J. M. H. Seed-crop size and eruptions of North American boreal seed-eating birds. J. Anim. Ecol. 70, 609–620 (2001).

    Article  Google Scholar 

  15. 15.

    Garrison, B. A., Koenig, W. D. & Knops, J. M. H. Spatial synchrony and temporal patterns in acorn production of California black oaks. In Proc. 6th Symposium on Oak Woodlands: Today’s Challenges, Tomorrow’s Opportunities. Pacific SW Forest and Range Experimental Station General Technical Report PSW-GTR-217 (eds Merenlender, A. et al.) 343–356 (USDA Forest Service, 2008).

  16. 16.

    Koenig, W. D. K. & Knops, J. M. H. Large-scale spatial synchrony and cross-synchrony in acorn production by two California oaks. Ecology 94, 83–93 (2013).

    Article  Google Scholar 

  17. 17.

    Liebhold, A. et al. Within-population spatialsynchrony in mast seeding of North American oaks. Oikos 104, 156–164 (2004).

    Article  Google Scholar 

  18. 18.

    LaMontagne, J. M. & Boutin, S. Local-scale synchrony and variability in mast seed production patterns of Picea glauca. J. Ecol. 95, 991–1000 (2007).

    Article  Google Scholar 

  19. 19.

    Koenig, W. D. & Knops, J. M. H. Patterns of annual seed production by Northern Hemisphere trees: a global perspective. Am. Nat. 155, 59–69 (2000).

    Article  Google Scholar 

  20. 20.

    Owens, J. N. & Blake, M. D. Forest Tree Seed Production: a review of the literature and recommendations for future research. Petawawa National Forestry Institute Information Report PI-X-53 (Canadian Forestry Service, 1985).

  21. 21.

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

    Article  Google Scholar 

  22. 22.

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

    Article  Google Scholar 

  23. 23.

    Royama, T. Analytical Population Dynamics (Chapman & Hall, 1992).

  24. 24.

    Koenig, W. D. Global patterns of environmental synchrony and the Moran effect. Ecography 25, 283–288 (2002).

    Article  Google Scholar 

  25. 25.

    Pearse, I. S., Koenig, W. D. & Kelly, D. Mechanisms of mast seeding: resources, weather, cues, and selection. N. Phytol. 212, 546–562 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Janzen, D. H. Seed predation by animals. Annu. Rev. Ecol. Syst. 2, 465–492 (1971).

    Article  Google Scholar 

  27. 27.

    Bogdziewicz, M. B. et al. Masting in wind-pollinated trees: system-specific roles of weather and pollination dynamics in driving seed production. Ecology 98, 2615–2625 (2017).

    Article  Google Scholar 

  28. 28.

    Selås, V., Piovesan, G., Adams, J. M. & Bernabei, M. Climatic factors controlling reproduction and growth of Norway spruce in southern Norway. Can. J. For. Res. 225, 217–225 (2002).

    Article  Google Scholar 

  29. 29.

    Schauber, E. M. et al. Masting by eighteen New Zealand plant species: the role of temperature as a synchronizing cue. Ecology 83, 1214–1225 (2002).

    Article  Google Scholar 

  30. 30.

    Roland, C. A., Schmidt, J. H. & Johnstone, J. F. Climate sensitivity of reproduction in a mast-seeding boreal conifer across its distributional range from lowland to treeline forests. Oecologia 174, 665–677 (2014).

    Article  Google Scholar 

  31. 31.

    Kelly, D. et al. Of mast and mean: differential-temperature cue makes mast seeding insensitive to climate change. Ecol. Lett. 16, 90–98 (2013).

    Article  Google Scholar 

  32. 32.

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

    Article  Google Scholar 

  33. 33.

    Sala, A., Hopping, K., Mcintire, E. J. B., Delzon, S. & Crone, E. E. Masting in whitebark pine (Pinus albicaulis) depletes stored nutrients. N. Phytol. 196, 189–199 (2012).

    CAS  Article  Google Scholar 

  34. 34.

    Vacchiano, G. et al. Spatial patterns and broad-scale weather cues of beech mast seeding in Europe. N. Phytol. 215, 595–608 (2017).

    Article  Google Scholar 

  35. 35.

    Koenig, W. D., Knops, J. M. H., Pesendorfer, M. B., Zaya, D. N. & Ashley, M. V. Drivers of synchrony of acorn production in the valley oak (Quercus lobata) at two spatial scales. Ecology 98, 3056–3062 (2017).

    Article  Google Scholar 

  36. 36.

    Ascoli, D. et al. Inter-annual and decadal changes in teleconnections drive continental-scale synchronization of tree reproduction. Nat. Commun. 8, 2205 (2017).

    Article  Google Scholar 

  37. 37.

    Dale, M., Francis, S., Krebs, C. J. & Nams, V. O. in Ecosystem Dynamics of the Boreal Forest: the Kluane Project. (eds Krebs, C. J. et al.) 116–137 (Oxford Univ. Press, 2001).

  38. 38.

    Nienstaedt, H. & Zasada, J. C. in Silvics of North America: Volume 1. Conifers Agricultural Handbook 654 (eds Burns, R. M. & Honkala, B. H.) 204–226 (Department of Agriculture and Forest Service, 1990).

  39. 39.

    Hijmans, R. J., Williams, E. & Vennes, C. geosphere: Spherical Trigonometry. R package version 1.5-7 (2017);

  40. 40.

    Thornton, M. M. et al. Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 3 (ORNL DAAC, 2016);

  41. 41.

    Koenig, W. D. & Knops, J. M. H. Testing for spatial autocorrelation in ecological studies. Ecography 21, 423–429 (1998).

    Article  Google Scholar 

  42. 42.

    Canty, A. & Ripley, B. boot: Bootstrap Functions. R package version 1.3-20. (2017).

  43. 43.

    Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. (2007).

  44. 44.

    Koenig, W. D., Walters, E. L. & Rodewald, P. G. Testing alternative hypotheses for the cause of population declines: the case of the red-headed woodpecker. Condor 119, 143–154 (2017).

    Article  Google Scholar 

  45. 45.

    Haynes, K. J. et al. Geographical variation in the spatial synchrony of a forest-defoliating insect: isolation of environmental and spatial drivers Proc. R. Soc. B 280, 20122373 (2013).

  46. 46.

    LaMontagne, J. M. & Boutin, S. Quantitative methods for defining mast-seeding years across species and studies. J. Veg. Sci. 20, 745–753 (2009).

    Article  Google Scholar 

  47. 47.

    Dormann, F. C. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article  Google Scholar 

  48. 48.

    Bjornstad, O. N. & Cai, J. ncf: spatial covariance functions. R package version 1.2-3 (2018);

  49. 49.

    Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).

    Article  Google Scholar 

  50. 50.

    Bates, D. et al. lme4: Linear mixed-effects models using ‘Eigen’ and S4. R package version 1.1-18-1 (2018);

  51. 51.

    Barton, K. MuMIn: Multi-model inference. R package version 1.43.6 (2019);

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We thank everyone who collected and contributed data, including C. Krebs and D. Pamalarek, and B. Chaudhary, M. Bell and B. Zuckerberg for providing feedback on the manuscript. Funding for this research was provided by NSF grants DEB-1745496 and DEB-1926341 to J.M.L. and NSF grant DEB-1256394 to W.D.K., as well as funding from the McIntire–Stennis program and a series (2005–2014) of NSERC grants to D.F.G. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Author information




Data were compiled by J.M.L, I.S.P. and W.D.K., and data were contributed by D.F.G. J.M.L. conducted the data analysis and wrote the manuscript with contributions from all of the authors.

Corresponding author

Correspondence to Jalene M. LaMontagne.

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The authors declare no competing interests.

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Peer review information Nature Plants thanks Magdalena Żywiec and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1

Summary of 68 datasets on white spruce reproduction including the number of sites in each region (n).

Extended Data Fig. 2

Multiple regression on distance matrices (MRM) results for spatial proximity and weather factors affecting mean synchrony of white spruce reproduction in year t.

Extended Data Fig. 3 Time series (1985–2014) of standardized annual white spruce reproduction with a minimum of six years of data.

Black lines represent individual time series from sites in five regions including: Alaska a, Yukon b, Alberta c, Ontario d, and Quebec e.

Extended Data Fig. 4 Model comparisons for the occurrence of mast years in white spruce.

Mast years are included as a binary response variable in generalized linear mixed effects models with temperature patterns and lag of mast years. ‘k’ represents the number of parameters in the model (see methods), ‘ΔAICc’ is the sample-size corrected AIC value compared to the best model, ‘wi’ is the weight, and cR2 is the conditional R2 for each model based on both fixed and random effects.

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LaMontagne, J.M., Pearse, I.S., Greene, D.F. et al. Mast seeding patterns are asynchronous at a continental scale. Nat. Plants 6, 460–465 (2020).

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