Population Ecology

, Volume 60, Issue 1–2, pp 77–87 | Cite as

Temporal variation in reproductive costs and payoffs shapes the flowering strategy of a neotropical milkweed, Asclepias curassavica

  • Kimberly M. KellettEmail author
  • Richard P. Shefferson
SPECIAL FEATURE: ORIGINAL ARTICLE Evolutionary demography: the dynamic and broad intersection of ecology and evolution


A central goal of evolutionary ecology is to understand the factors that select for particular life history strategies, such as delaying reproduction. For example, environmental variation and reproductive costs to survival and growth often select for reproductive delays in semelparous and iteroparous species. In this study, we examine how variation in reproductive cost, which we define as a reduction to growth, survival, or future reproduction after a reproductive event, may select for reproductive delay in an iteroparous Neotropical milkweed with no obvious reproductive season. We analyzed demographic data collected every 3 months for 3 years from four populations of Asclepias curassavica in Monteverde, Costa Rica. We detected costs of flowering to survival and growth that varied in magnitude between our 12 transition periods without a seasonal pattern. The populations also exhibited temporal variation in reproductive payoffs measured as seedling establishment. We incorporated these reproductive costs into demographic projection models, which predicted a delayed flowering strategy only when we included temporal variation in costs and payoffs. Temporal variation in reproductive costs and payoffs is an important selective force in the evolution of delayed flowering in iteroparous species. Further, a lack of predictable seasonal pattern to reproductive costs and payoffs may contribute to the lack of seasonal reproductive patterns observed in our study species and other Neotropical species.


Demography Life history evolution Optimality models Reproductive costs Stochastic population models 



We thank J. Watkins, T. Bakke, K. Schultz, L. Kline, and L. Beveridge for their help with data collection in the field, E. Goolsby, and E. Caughlin for helpful discussion on the development on this project, S. Wenger for statistical advice, T. Dallas for computational assistance, and T. Kartzinel and D. Humphreys for providing feedback on the manuscript. We appreciate funding from the Odum School of Ecology, The Tinker Foundation, and Sigma Xi.


Funding for this research provided in part by Sigma Xi Grants in Aid of Research Grant ID:. G2012162529 and a Graduate Field Research Award from The Tinker Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10144_2018_618_MOESM1_ESM.pdf (209 kb)
Supplementary material 1 (PDF 208 KB)


  1. Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48CrossRefGoogle Scholar
  2. Bazzaz FA, Chiariello NR, Coley PD, Pitelka LF (1987) Allocating resources to reproduction and defense. Bioscience 37:58–67CrossRefGoogle Scholar
  3. Bell G (1980) The costs of reproduction and their consequences. Am Nat 116:45–76CrossRefGoogle Scholar
  4. Boyce MS, Haridas CV, Lee CT, NCEAS Stochastic Demography Working Group (2006) Demography in an increasingly variable world. Trends Ecol Evol 21:141–148CrossRefPubMedGoogle Scholar
  5. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  6. Childs DZ, Rees M, Rose KE, Grubb PJ, Ellner SP (2004) Evolution of size-dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model. Proc R Soc Lond B 271:425–434CrossRefGoogle Scholar
  7. Childs DZ, Metcalf C, Rees M (2010) Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. Proc R Soc Lond B 277:3055–3064CrossRefGoogle Scholar
  8. Cody ML (1966) A general theory of clutch size. Evolution 2:174–184CrossRefGoogle Scholar
  9. Croat TB (1978) Flora of Barro Colorado Island. Stanford University Press, Palo AltoGoogle Scholar
  10. Crozier LG, Hendry A, Lawson PW, Quinn T, Mantua N, Battin J, Shaw R, Huey R (2008) Potential responses to climate change in organisms with complex life histories: evolution and plasticity in Pacific salmon. Evol Appl 1:252–270CrossRefPubMedPubMedCentralGoogle Scholar
  11. Easterling MR, Ellner SP, Dixon PM (2000) Size-specific sensitivity: applying a new structured population model. Ecology 81:694–708CrossRefGoogle Scholar
  12. Gremer JR, Venable DL (2014) Bet hedging in desert winter annual plants: optimal germination strategies in a variable environment. Ecol Lett 17:380–387CrossRefPubMedGoogle Scholar
  13. Hart R (1977) Why are biennials so few? Am Nat 111:792–799CrossRefGoogle Scholar
  14. Horvitz CC, Schemske DW (1988) Demographic cost of reproduction in a neotropical herb: an experimental field study. Ecology 69:1741–1745CrossRefGoogle Scholar
  15. Jacquemyn H, Brys R, Jongejans E (2010) Size-dependent flowering and costs of reproduction affect population dynamics in a tuberous perennial woodland orchid. J Ecol 98:1204–1215CrossRefGoogle Scholar
  16. Koons DN, Metcalf CJE, Tuljapurkar S (2008) Evolution of delayed reproduction in uncertain environments: a life-history perspective. Am Nat 172:797–805CrossRefPubMedGoogle Scholar
  17. Kozłowski J, Wiegert RG (1986) Optimal allocation of energy to growth and reproduction. Theor Popul Biol 29:16–37CrossRefPubMedGoogle Scholar
  18. Law R (1979) Ecological determinants in the evolution of life histories. In: Anderson RM, Turner BD, Taylor LR (eds) Population dynamics. Blackwell, Oxford, pp 81–103Google Scholar
  19. Malcolm SB (1991) Cardenolide-mediated interactions between plants and herbivores. In: Rosenthal GA, Berenbaum MR (eds) Herbivores: their interactions with secondary plant metabolites, 2nd edn. Academic Press, San Diego, pp 251–296CrossRefGoogle Scholar
  20. Mangel M (1994) Climate change and salmonid life history variation. Deep Sea Res Part II 41:75–106CrossRefGoogle Scholar
  21. Metcalf CJE, Pavard S (2007) Why evolutionary biologists should be demographers. Trends Ecol Evol 22:205–212CrossRefPubMedGoogle Scholar
  22. Metcalf CJE, Rose KE, Rees M (2003) Evolutionary demography of monocarpic perennials. Trends Ecol Evol 18:471–480CrossRefGoogle Scholar
  23. Metcalf CJE, Rose KE, Childs DZ, Sheppard AW, Grubb PJ (2008) Evolution of flowering decisions in a stochastic, density-dependent environment. Proc Natl Acad Sci USA 105:10466–10470CrossRefPubMedGoogle Scholar
  24. Miller TE, Williams JL, Jongejans E, Brys R, Jacquemyn H (2012) Evolutionary demography of iteroparous plants: incorporating non-lethal costs of reproduction into integral projection models. Proc R Soc Lond B 279:2831–2840CrossRefGoogle Scholar
  25. Mole S (1994) Trade-offs and constraints in plant-herbivore defense theory: a life-history perspective. Oikos 71:3–12CrossRefGoogle Scholar
  26. Obeso JR (2002) The costs of reproduction in plants. New Phytol 155:321–348CrossRefGoogle Scholar
  27. Orzack SH (1993) Life history evolution and population dynamics in variable environments: some insights from stochastic demography. In: Yoshimura J, Clark CW (eds) Adaptation in stochastic environments. Springer, New York, pp 63–104CrossRefGoogle Scholar
  28. Primack RB, Hall P (1990) Costs of reproduction in the pink lady’s slipper orchid: a four-year experimental study. Am Nat 136:638–656CrossRefGoogle Scholar
  29. Proaktor GT, Coulson T, Milner-Gulland E (2008) The demographic consequences of the cost of reproduction in ungulates. Ecology 89:2604–2611CrossRefPubMedGoogle Scholar
  30. R Core Team (2014) R: a language and environment for statistical computing R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  31. Rees M, Ellner SP (2009) Integral projection models for populations in temporally varying environments. Ecol Monogr 79:575–594CrossRefGoogle Scholar
  32. Rees M, Childs DZ, Rose KE, Grubb PJ (2004) Evolution of size-dependent flowering in a variable environment: partitioning the effects of fluctuating selection. Proc R Soc Lond B 271:471–475CrossRefGoogle Scholar
  33. Rees M, Childs DZ, Metcalf CJE, Rose KE, Sheppard AW, Grubb PJ (2006) Seed dormancy and delayed flowering in monocarpic plants: selective interactions in a stochastic environment. Am Nat 168:53–71CrossRefGoogle Scholar
  34. Reznick D, Nunney L, Tessier A (2000) Big houses, big cars, superfleas and the costs of reproduction. Trends Ecol Evol 15:421–425CrossRefPubMedGoogle Scholar
  35. Roff DA (2002) Life history evolution. Sinauer Associates, SunderlandGoogle Scholar
  36. Shefferson RP, Warren RJ, Pulliam HR (2014) Life-history costs make perfect sprouting maladaptive in two herbaceous perennials. J Ecol 102:1318–1328CrossRefGoogle Scholar
  37. Sletvold N, Ågren J (2011) Among-population variation in costs of reproduction in the long-lived orchid Gymnadenia conopsea: an experimental study. Oecologia 167:461–468CrossRefPubMedPubMedCentralGoogle Scholar
  38. Sletvold N, Ågren J (2015a) Nonlinear costs of reproduction in a long-lived plant. J Ecol 103:1205–1213CrossRefGoogle Scholar
  39. Sletvold N, Ågren J (2015b) Climate-dependent costs of reproduction: Survival and fecundity costs decline with length of the growing season and summer temperature. Ecol Lett 18:357–364CrossRefPubMedGoogle Scholar
  40. Stearns SC (1992) The evolution of life histories. Oxford University Press, OxfordGoogle Scholar
  41. Stearns SC, Crandall RE (1984) Plasticity for age and size at sexual maturity: a life-history response to unavoidable stress. In: Potts GW, Wooton RJ (eds) Fish reproduction: strategies and tactics. Academic Press, London, pp 13–33Google Scholar
  42. Tuljapurkar S (1990) Population dynamics in variable environments. Springer, New YorkCrossRefGoogle Scholar
  43. Wells T (1967) Changes in a population of Spiranthes spiralis (L.) Chevall. at Knocking Hoe National Nature Reserve, Bedfordshire, 1962–65. J Ecol 55:83–99CrossRefGoogle Scholar
  44. Wesselingh RA, Klinkhamer PG, de Jong TJ, Boorman LA (1997) Threshold size for flowering in different habitats: effects of size-dependent growth and survival. Ecology 78:2118–2132Google Scholar
  45. Williams JL, Jacquemyn H, Ochocki BM, Brys R, Miller TX (2015) Life history evolution under climate change and its influence on the population dynamics of a long-lived plant. J Ecology 103:798–808CrossRefGoogle Scholar
  46. Wyatt R, Broyles SB (1997) The weedy tropical milkweeds Asclepias curassavica and A. fruticosa are self-compatible. Biotropica 29:232–234CrossRefGoogle Scholar

Copyright information

© The Society of Population Ecology and Springer Japan KK, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Life and Earth Sciences, Perimeter CollegeGeorgia State UniversityDunwoodyUSA
  2. 2.Organization for Programs on Environmental Science, Graduate School of Arts and SciencesUniversity of TokyoTokyoJapan
  3. 3.Odum School of EcologyUniversity of GeorgiaAthensUSA

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