Plant Ecology

, Volume 220, Issue 1, pp 97–109 | Cite as

Serotiny in the South African shrub Protea repens is associated with gradients of precipitation, temperature, and fire intensity

  • Roland C. de GouvenainEmail author
  • Jeremy J. Midgley
  • Cory Merow


Globally, variability in canopy seed retention within closed cones (serotiny) among fire-adapted plant species is often associated with gradients in fire regime. Few studies have investigated the association of intraspecific variation in serotiny with geographical variation in fire and other environmental factors, especially climatic ones, and none has done so in the Cape Floristic Region (CFR) of South Africa. Yet the relationship between environmental gradients and intraspecific variation in reproductive traits may help us understand if those gradients partly shaped the evolution of the rich diversity or proteas in the CFR, and predict the resilience of fire-adapted Protea species to climate and fire regime changes in the CFR. We examined the association of the variability in serotiny in Protea repens (L.) L, the Common Sugarbush, with gradients in fire regime and climatic factors, and with plant age, cone age, and age of oldest closed cone across its 700 km-long longitudinal range in the CFR. Cone age was a significant covariate of the probability that a given cone was closed (our measure of serotiny), but plant age and age of oldest closed cone were not. Variability in the degree of serotiny was significant among populations. Serotiny was highest where fire intensity was historically high, where both mean annual precipitation and mean annual temperature were low, and where rainfall was least seasonal, but fire frequency was not a predictor of serotiny.


Canopy seed bank Cape Floristic Region Intraspecific variability Fire adaptation Fire regime 



We thank Jasper Slingsby, Tony Rebelo, Tom Slingsby, and Nick Lindenberg for their help. Kobus Kellerman and Jocelyne de Gouvenain assisted with the field sampling. Jane Carlson generously provided UTM coordinates of P. repens populations. John Silander Jr. reviewed the draft manuscript. We thank CapeNature, the Eastern Cape Parks and Tourism Agency, and the South African National Parks for permission to sample populations of P. repens under their jurisdiction. RdG thanks the University of Cape Town Biological Sciences Department for in-kind support and JM’s hospitality while conducting sabbatical research in his lab. We thank three anonymous reviewers for their thorough and constructive comments.


  1. Akman M, Carlson JE, Holsinger KE, Latimer AM (2016) Transcriptome sequencing reveals population differentiation in gene expression linked to functional traits and environmental gradients in the South African shrub Protea repens. New Phytol 210:295–309CrossRefGoogle Scholar
  2. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Soft. Google Scholar
  3. Bond WJ (1984) Fire survival of Cape Proteaceae: influence of fire season and seed predators. Vegetatio 56:65–74Google Scholar
  4. Bond WJ (2003) Fire. In: Cowling RM, Richardson DM, Pierce SM (eds) Vegetation of Southern Africa. Cambridge University Press, Cambridge, pp 421–446Google Scholar
  5. Bond WJ, Keeley JE (2005) Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems. Trends Ecol Evol 20:387–394CrossRefGoogle Scholar
  6. Buma B, Brown CD, Donato DC, Fontaine JB, Johnstone JF (2013) The impacts of changing disturbance regimes on serotinous plant populations and communities. Bioscience 63:866–876CrossRefGoogle Scholar
  7. Burnham KP, Anderson DR (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261–304CrossRefGoogle Scholar
  8. Carlson JE, Adams CA, Holsinger KE (2015) Intraspecific variation in stomatal traits, leaf traits and physiology reflects adaptation along aridity gradients in a South African shrub. Ann Bot 117(1):195–207CrossRefGoogle Scholar
  9. Cowling RM, Lamont BB (1985) Variation in serotiny of three Banksia species along a climatic gradient. Austral Ecol 10:345–350CrossRefGoogle Scholar
  10. Cramer MD, Midgley JJ (2009) Maintenance costs of serotiny do not explain weak serotiny. Austral Ecol 34:653–662CrossRefGoogle Scholar
  11. de Gouvenain RC, Delgadillo J (2012) Geographical variation in population demography and life history traits of Tecate cypress (Hesperocyparis forbesii) suggests a fire regime gradient across the USA–Mexico border. Plant Ecol 213:723–733CrossRefGoogle Scholar
  12. de Klerk HM (2008) A pragmatic assessment of the usefulness of the MODIS (Terra and Aqua) 1-km active fire (MOD14A2 and MYD14A2) products for mapping fires in the fynbos biome. Int J Wildland Fire 17:166–178CrossRefGoogle Scholar
  13. Enright NJ, Lamont BB (1989) Fire temperatures and follicle-opening requirements in 10 Banksia species. Austral Ecol 14:107–113CrossRefGoogle Scholar
  14. Enright NJ, Marsula R, Lamont BB, Wissel C (1998) The ecological significance of canopy seed storage in fire-prone environments: a model for non-sprouting shrubs. J Ecol 86:946–959CrossRefGoogle Scholar
  15. Enright NJ, Fontaine JB, Lamont BB, Miller BP, Westcott VC (2014) Resistance and resilience to changing climate and fire regime depend on plant functional traits. J Ecol 102:1572–1581CrossRefGoogle Scholar
  16. Gauthier S, Bergeron Y, Simon JP (1996) Effects of fire regime on the serotiny level of jack pine. J Ecol 84:539–548CrossRefGoogle Scholar
  17. Giglio L, Descloitres J, Justice CO, Kaufman YJ (2003) An enhanced contextual fire detection algorithm for MODIS. Remote Sens Environ 87:273–282CrossRefGoogle Scholar
  18. Giglio L, Csiszar I, Justice CO (2006a) Global distribution and seasonality of active fires as observed with the Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. J Geophys Res 111(G2):1–12CrossRefGoogle Scholar
  19. Giglio L, Van Der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006b) Global estimation of burned area using MODIS active fire observations. Atmos Chem Phys 6:957–974CrossRefGoogle Scholar
  20. Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens Environ 178:31–41CrossRefGoogle Scholar
  21. Givnish TJ (1981) Serotiny, geography, and fire in the Pine Barrens of New Jersey. Evolution 35:101–123CrossRefGoogle Scholar
  22. Goubitz S, Nathan R, Roitemberg R, Shmida A, Ne’eman G (2004) Canopy seed bank structure in relation to: fire, tree size and density. Plant Ecol 173:191–201CrossRefGoogle Scholar
  23. Groom PK, Lamont BB (1997) Fruit-seed relations in Hakea: serotinous species invest more dry matter in predispersal seed protection. Austral Ecol 22:352–355CrossRefGoogle Scholar
  24. Harris W (2002) Variation of inherent seed capsule splitting in populations of Leptospermum scoparium (Myrtaceae) in New Zealand. N Z J Bot 40:405–417CrossRefGoogle Scholar
  25. Hilborn R, Mangel M (1997) The ecological detective. Princeton University Press, PrincetonGoogle Scholar
  26. Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sens Environ 83:244–262CrossRefGoogle Scholar
  27. Keeley JE, Fotheringham CJ (2001) Historic fire regime in Southern California shrublands. Conserv Biol 15:1536–1548CrossRefGoogle Scholar
  28. Keeley JE, Zedler PH (2009) Large, high-intensity fire events in southern California shrublands: debunking the fine-grain age patch model. Ecol Appl 19:69–94CrossRefGoogle Scholar
  29. Ladd PG, Midgley JJ, Nield AP (2013) Serotiny in southern hemisphere conifers. Aust J Bot 61:486–496CrossRefGoogle Scholar
  30. Lamont BB, Enright NJ (2000) Adaptive advantages of aerial seed banks. Plant Species Biol 15:157–166CrossRefGoogle Scholar
  31. Lamont BB, He T (2012) Fire-adapted Gondwanan angiosperm floras evolved in the Cretaceous. BMC Evol Biol 12:1–11CrossRefGoogle Scholar
  32. Lamont BB, He T (2016) Fire-proneness as a prerequisite for the evolution of fire-adapted traits. Trends Plant Sci 22(4):278–288CrossRefGoogle Scholar
  33. Lamont BB, Le Maitre DC, Cowling RM, Enright NJ (1991) Canopy seed storage in woody plants. Bot Rev 57:277–317CrossRefGoogle Scholar
  34. Lamont BB, Written VA, Witkowski ETF, Rees RG, Enright NJ (1994) Regional and local (road verge) effects on size and fecundity in Banksia menziesii. Aust J Ecol 19:197–205CrossRefGoogle Scholar
  35. Lamont BB, He T, Downes KS (2013) Adaptive responses to directional trait selection in the Miocene enabled Cape proteas to colonize the savanna grasslands. Evol Ecol 27:1099–1115CrossRefGoogle Scholar
  36. Latimer A, Silander JA Jr, Rebelo AG, Midgley GF (2009) Experimental biogeography: the role of environmental gradients in high geographic diversity in Cape Proteaceae. Oecologia 160:151–162CrossRefGoogle Scholar
  37. Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, Seidl R, Delzon S, Corona P, Kolström M, Lexer MJ, Marchetti M (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manage 259:698–709CrossRefGoogle Scholar
  38. Logan M (2010) Biostatistical design and analysis using R. Wiley, OxfordCrossRefGoogle Scholar
  39. Malcolm JR, Liu C, Neilson RP, Hansen L, Hannah L (2006) Global warming and extinctions of endemic species from biodiversity hotspots. Conserv Biol 20:538–548CrossRefGoogle Scholar
  40. Manry DE, Knight RS (1986) Lightning density and burning frequency in South African vegetation. Vegetatio 66:67–76Google Scholar
  41. Merow C, Latimer AM, Wilson AM, McMahon SM, Rebelo AG, Silander JA Jr (2014) On using integral projection models to generate demographically driven predictions of species’ distributions: development and validation using sparse data. Ecography 37:1167–1183CrossRefGoogle Scholar
  42. Midgley J (2000) What are the relative costs, limits and correlates of increased degree of serotiny? Austral Ecol 25:65–68CrossRefGoogle Scholar
  43. Midgley J, Bond W (2011) Pushing back in time: the role of fire in plant evolution. New Phytol 191:5–7CrossRefGoogle Scholar
  44. Midgley J, Enright NJ (2000) Serotinous species show correlation between retention time for leaves and cones. J Ecol 88:348–351CrossRefGoogle Scholar
  45. Minnich RA (2001) An integrated model of two fire regimes. Conserv Biol 15:1549–1553CrossRefGoogle Scholar
  46. Mitchell N, Moore TE, Mollmann HK, Carlson JE, Mocko K, Martinez-Cabrera H, Adams C, Silander JA Jr, Jones CS, Schlichting CD, Holsinger KE (2015) Functional traits in parallel evolutionary radiations and trait-environment associations in the Cape Floristic Region of South Africa. Am Nat 185:525–537CrossRefGoogle Scholar
  47. Muir PS, Lotan JE (1985) Disturbance history and serotiny of Pinus contorta in western Montana. Ecology 66:1658–1668CrossRefGoogle Scholar
  48. NASA (National Aeronautic and Space Administration) (2017) Fire information for resource management Ssystem (FIRMS). Accessed 5 June 2017
  49. Parchman TL, Gompert Z, Mudge J, Schilkey FD, Benkman CW, Buerkle CA (2012) Genome-wide association genetics of an adaptive trait in lodgepole pine. Mol Ecol 21:2991–3005CrossRefGoogle Scholar
  50. Pausas JG, Bradstock RA, Keith DA, Keeley JE (2004) Plant functional traits in relation to fire in crown-fire ecosystems. Ecology 85:1085–1100CrossRefGoogle Scholar
  51. Prunier R, Holsinger KE (2010) Was it an explosion? Using population genetics to explore the dynamics of a recent radiation within Protea (Proteaceae L.). Mol Ecol 19:3968–3980CrossRefGoogle Scholar
  52. QGIS Development Team (2017) QGIS Geographic Information System. Open Source Geospatial Foundation Project.
  53. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  54. Rebelo T (1995) Sasol proteas: a field guide to the proteas of Southern Africa. Fernwood Press (Pty) Ltd., PretoriaGoogle Scholar
  55. Scheiter S, Higgins SI, Osborne CP, Bradshaw C, Lunt D, Ripley BS, Taylor LL, Beerling DJ (2012) Fire and fire-adapted vegetation promoted C4 expansion in the late Miocene. New Phytol 195:653–666CrossRefGoogle Scholar
  56. Schnitzler J, Barraclough TG, Boatwright JS, Goldblatt P, Manning JC, Powell MP, Rebelo T, Savolainen V (2011) Causes of plant diversification in the Cape biodiversity hotspot of South Africa. Syst Biol 60:343–357CrossRefGoogle Scholar
  57. Schoennagel T, Turner MG, Romme WH (2003) The influence of fire interval and serotiny on postfire lodgepole pine density in Yellowstone National Park. Ecology 84:2967–2978CrossRefGoogle Scholar
  58. Schulze RE (2007) South African atlas of climatology and agrohydrology. Water Research Commission, PretoriaGoogle Scholar
  59. Schwilk DW, Ackerly DD (2001) Flammability and serotiny as strategies: correlated evolution in pines. Oikos 94:326–336CrossRefGoogle Scholar
  60. Scott L, Anderson HM, Anderson JM (2003) Vegetation history. In: Cowling RM, Richardson DM, Pierce SM (eds) Vegetation of Southern Africa. Cambridge University Press, Cambridge, pp 62–84Google Scholar
  61. Tapias R, Climent J, Pardos JA, Gil L (2004) Life histories of Mediterranean pines. Plant Ecol 171:53–68CrossRefGoogle Scholar
  62. Tonnabel J, Van Dooren TJM, Midgley J, Haccou P, Mignot A, Ronce O, Olivieri I (2012) Optimal resource allocation in a serotinous non-resprouting plant species under different fire regimes. J Ecol 100:1464–1474CrossRefGoogle Scholar
  63. Tonnabel J, Mignot A, Douzery EJP, Rebelo AG, Schurr FM, Midgley J, Illing N, Justy F, Orcel D, Olivieri I (2014) Convergent and correlated evolution of major life-history traits in the angiosperm genus Leucadendron (Proteaceae). Evolution 68:2775–2792CrossRefGoogle Scholar
  64. Wills TJ (2003) Using Banksia (Proteaceae) node counts to estimate time since fire. Aust J Bot 51:239–242CrossRefGoogle Scholar
  65. Wilson AM, Latimer AM, Silander JA Jr (2015) Climatic controls on ecosystem resilience: postfire regeneration in the Cape Floristic Region of South Africa. Proc Natl Acad Sci 112:9058–9063CrossRefGoogle Scholar
  66. Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New YorkCrossRefGoogle Scholar
  67. Zuur A, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Biology DepartmentRhode Island CollegeProvidenceUSA
  2. 2.Department of Biological SciencesUniversity of Cape TownCape TownSouth Africa
  3. 3.Department of Ecology and Evolutionary BiologyYale UniversityNew HavenUSA

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