Evolutionary Ecology

, Volume 33, Issue 6, pp 811–824 | Cite as

Artificial seed aging reveals the invisible fraction: Implications for evolution experiments using the resurrection approach

  • Steven J. FranksEmail author
  • Michael R. Sekor
  • Samuel Davey
  • Arthur E. Weis
Original Paper


Non-random mortality is a key driver of evolution, but mortality that occurs early in life leaves adult traits of individuals that died unknown. This can lead to the invisible fraction problem, which causes difficulty in measuring selection and evolution in natural and experimental populations. Furthermore, seeds or other propagules that are stored intentionally or that persist in dormant states in nature can experience storage conditions that alter adult traits. Invisible fraction and storage condition effects can cause bias in evolutionary studies such as those using the resurrection approach of comparing ancestors and descendants in common environments. To investigate invisible fraction and storage condition effects, we subjected seeds of Brassica rapa Fast Plants to artificial aging under hot, humid conditions. We grew plants from artificially aged seeds alongside unaged control seeds for two generations and measured morphological and phenological traits on adult plants. We found that the plants from artificially aged seeds flowered later than those from unaged seeds in both the first and second generation, indicating storage condition and invisible fraction biases. However, the difference in flowering time was smaller in the second generation, indicating that the refresher generation decreased the storage condition effect. We also found that seeds that survived artificial aging were smaller than seeds that did not survive, indicating a potential physical basis for non-random mortality in storage. These results suggest that invisible fraction and storage condition effects can bias the results of resurrection experiments, and that the proper storage of seeds for use in resurrection experiments, as well as a refresher generation, are critical for valid results. The results also demonstrate that artificial aging can be used as a tool for studying mortality of propagules in nature, such as in soil seed banks, thus providing insight into evolutionary processes that would otherwise remain obscure.


Adaptation Brassica rapa Climate change Contemporary evolution Missing data problem Phenology Seed storage 



We thank the Fordham University Honors Program for providing support to S.D. for his research.


This work was supported by grants from the National Science Foundation (DEB-1142784 and IOS-1546218) to S.J.F., and an NSERC Discovery Grant to A.E.W.

Compliance with ethical standards

Conflict of interest

The authors declare no conflicts of interest.


  1. Agren J, Schemske DW (1992) Artificial selection on trichome number in Brassica rapa. Theor Appl Genet 83:673–678CrossRefGoogle Scholar
  2. Berteaux D, Réale D, McAdam AG, Boutin S (2004) Keeping pace with fast climate change: can arctic life count on evolution? Integr Comp Biol 44:140–151. CrossRefPubMedGoogle Scholar
  3. Chaitanya KSK, Naithani SC (1994) Role of superoxide, lipid peroxidation and superoxide dismutase in membrane perturbation during loss of viability in seeds of Shorea robusta Gaertn.f. New Phytol 126:623–627. CrossRefGoogle Scholar
  4. Delouche JC, Baskin CC (1973) Accelerated aging techniques for predicting the relative storability of seed lots. Seed Sci Technol 1:427–452Google Scholar
  5. Donohue K (2002) Germination timing influences natural selection on life-history characters in Arabidopsis thaliana. Ecology 83:1006–1016CrossRefGoogle Scholar
  6. Ellis RH, Hong TD, Roberts EH (2008) Seed moisture content, storage, viability and vigour. Seed Sci Res 1:275–279. CrossRefGoogle Scholar
  7. Elwell A, Durham TL, Miller ND, Spalding E (2007) Environmental effects on seed size and effects of seed size on seedling development in Arabidopsis. Plant Biol Rockv 2007:16Google Scholar
  8. Elwell AL, Gronwall DS, Miller ND, Spalding EP, Brooks TL (2011) Separating parental environment from seed size effects on next generation growth and development in Arabidopsis. Plant Cell Environ 34:291–301. CrossRefPubMedGoogle Scholar
  9. Etterson JR et al (2016) Project baseline: an unprecedented resource to study plant evolution across space and time. Am J Bot 103:164–173. CrossRefPubMedGoogle Scholar
  10. Falahati-Anbaran M, Lundemo S, Stenøien HK (2014) Seed dispersal in time can counteract the effect of gene flow between natural populations of Arabidopsis thaliana. New Phytol 202:1043–1054. CrossRefPubMedGoogle Scholar
  11. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics. Longman, HarlowGoogle Scholar
  12. Fisher RA (1958) The genetical theory of natural selection. Claredon, New YorkGoogle Scholar
  13. Fleming MB, Patterson EL, Reeves PA, Richards CM, Gaines TA, Walters C (2018) Exploring the fate of mRNA in aging seeds: protection, destruction, or slow decay? J Exp Bot 69:4309–4321. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Franks SJ, Sim S, Weis AE (2007) Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc Natl Acad Sci USA 104:1278–1282. CrossRefPubMedGoogle Scholar
  15. Franks SJ et al (2008) The resurrection initiative: storing ancestral genotypes to capture evolution in action. Bioscience 58:870–873. CrossRefGoogle Scholar
  16. Franks SJ, Weber JJ, Aitken SN (2014) Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol Appl 7:123–139. CrossRefPubMedGoogle Scholar
  17. Franks SJ, Hamann E, Weis AE (2018) Using the resurrection approach to understand contemporary evolution in changing environments. Evol Appl 11:17–28. CrossRefPubMedGoogle Scholar
  18. Grafen A (1988) On the uses of data on lifetime reproductive success. In: Clutton-Brock TH (ed) Reproductive success. University of Chicago Press, Chicago, pp 454–471Google Scholar
  19. IPCC (2014) 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. Cambridge University Press, CambridgeGoogle Scholar
  20. Jezkova T, Wiens JJ (2016) Rates of change in climatic niches in plant and animal populations are much slower than projected climate change. Proc R Soc B Biol Sci. CrossRefGoogle Scholar
  21. Kitashiba H, Nasrallah JB (2014) Self-incompatibility in Brassicaceae crops: lessons for interspecific incompatibility. Breed Sci 64:23–37. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Li DZ, Pritchard HW (2009) The science and economics of ex situ plant conservation. Trends Plant Sci 14:614–621. CrossRefPubMedGoogle Scholar
  23. Murthy UM, Kumar PP, Sun WQ (2003) Mechanisms of seed ageing under different storage conditions for Vigna radiata (L.) Wilczek: lipid peroxidation, sugar hydrolysis, Maillard reactions and their relationship to glass state transition. J Exp Bot 54:1057–1067CrossRefGoogle Scholar
  24. Nagel M, Börner A (2009) The longevity of crop seeds stored under ambient conditions. Seed Sci Res 20:1–12. CrossRefGoogle Scholar
  25. Nevo E, Fu Y-B, Pavlicek T, Khalifa S, Tavasi M, Beiles A (2012) Evolution of wild cereals during 28 years of global warming in Israel. Proc Natl Acad Sci 109:3412–3415. CrossRefPubMedGoogle Scholar
  26. Nguyen TP, Keizer P, van Eeuwijk F, Smeekens S, Bentsink L (2012) Natural variation for seed longevity and seed dormancy are negatively correlated in Arabidopsis. Plant Physiol 160:2083–2092. CrossRefPubMedPubMedCentralGoogle Scholar
  27. Nunney L (2002) The effective size of annual plant populations: the interaction of a seed bank with fluctuating population size in maintaining genetic variation. Am Nat 160:195–204. CrossRefPubMedGoogle Scholar
  28. Pammenter NW, Adamson JH, Berjak P (1974) Viability of stored seed: extension by cathodic protection. Science 186:1123–1124. CrossRefPubMedGoogle Scholar
  29. Pelletier F, Garant D, Hendry AP (2009) Eco-evolutionary dynamics. Philos Trans R Soc B Biol Sci 364:1483–1489. CrossRefGoogle Scholar
  30. Pulido F, Berthold P (2004) Microevolutionary response to climatic change. Birds Clim Change 35:151–183. CrossRefGoogle Scholar
  31. Rajjou L, Lovigny Y, Groot SPC, Belghazi M, Job C, Job D (2008) Proteome-wide characterization of seed aging in Arabidopsis: a comparison between artificial and natural aging protocols. Plant Physiol 148:620–641. CrossRefPubMedPubMedCentralGoogle Scholar
  32. R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  33. Roach DA, Wulff RD (1987) Maternal effects in plants. Annu Rev Ecol Syst 18:209–235. CrossRefGoogle Scholar
  34. Salamin N, Wuest RO, Lavergne S, Thuiller W, Pearman PB (2010) Assessing rapid evolution in a changing environment. Trends Ecol Evol 25:692–698. CrossRefPubMedGoogle Scholar
  35. Schwember AR, Bradford KJ (2010) Quantitative trait loci associated with longevity of lettuce seeds under conventional and controlled deterioration storage conditions. J Exp Bot 61:4423–4436. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Sekor MR, Franks SJ (2018) An experimentally introduced population of Brassica rapa (Brassicaceae). 2. Rapid evolution of phenotypic traits. Plant Ecol Evol 151:293–302. CrossRefGoogle Scholar
  37. Skelly D (2010) A climate for contemporary evolution. BMC Biol 8:136–136. CrossRefPubMedPubMedCentralGoogle Scholar
  38. Thomann M, Imbert E, Engstrand RC, Cheptou PO (2015) Contemporary evolution of plant reproductive strategies under global change is revealed by stored seeds. J Evol Biol 28:766–778. CrossRefPubMedGoogle Scholar
  39. Thompson JN (1998) Rapid evolution as an ecological process. Trends Ecol Evol 13:329–332. CrossRefPubMedGoogle Scholar
  40. Thompson JN (2013) Relentless evolution. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  41. Torres M, De Paula M, Pérez-Otaola M, Darder M, Frutos G, Martínez-Honduvilla CJ (1997) Ageing-induced changes in glutathione system of sunflower seeds. Physiol Plant 101:807–814. CrossRefGoogle Scholar
  42. Vertucci CW, Roos EE (1990) Theoretical basis of protocols for seed storage. Plant Physiol 94:1019–1023. CrossRefPubMedPubMedCentralGoogle Scholar
  43. Walters C (2008) Understanding the mechanisms and kinetics of seed aging. Seed Sci Res 8:223–244. CrossRefGoogle Scholar
  44. Walters C, Berjak P, Pammenter N, Kennedy K, Raven P (2013) Preservation of recalcitrant seeds. Science 339:915–916. CrossRefPubMedGoogle Scholar
  45. Weis AE (2018) Detecting the “invisible fraction” bias in resurrection experiments. Evol Appl 11:88–95. CrossRefPubMedGoogle Scholar
  46. Williams PH, Hill CB (1986) Rapid-cycling populations of Brassica. Science 232:1385–1389CrossRefGoogle Scholar
  47. Wulff RD (1986) Seed size variation in Desmodium paniculatum: II. Effects on seedling growth and physiological performance. J Ecol 74:99–114. CrossRefGoogle Scholar
  48. Yin X, He D, Gupta R, Yang P (2015) Physiological and proteomic analyses on artificially aged Brassica napus seed. Front Plant Sci 6:112. CrossRefPubMedPubMedCentralGoogle Scholar
  49. Zu P, Blanckenhorn WU, Schiestl FP (2016) Heritability of floral volatiles and pleiotropic responses to artificial selection in Brassica rapa. New Phytol 209:1208–1219. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Steven J. Franks
    • 1
    Email author
  • Michael R. Sekor
    • 1
  • Samuel Davey
    • 1
  • Arthur E. Weis
    • 2
    • 3
  1. 1.Department of Biological SciencesFordham UniversityBronxUSA
  2. 2.Department of Ecology and Evolutionary BiologyUniversity of TorontoTorontoCanada
  3. 3.Koffler Scientific Reserve at Jokers HillUniversity of TorontoTorontoCanada

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