Climatic Change

, Volume 117, Issue 4, pp 919–931 | Cite as

Potential spread of recently naturalised plants in New Zealand under climate change



Climate change and biological invasions are major causes of biodiversity loss and may also have synergistic effects, such as range shifts of invaders due to changing climate. Bioclimatic models provide an important tool to assess how the threat of invasive species may change with altered temperature and precipitation regimes. In this study, potential distributions of three recently naturalised plant species in New Zealand are modelled (Archontophoenix cunninghamiana, Psidium guajava and Schefflera actinophylla), using four different general circulation models (CCCMA-CGCM3, CSIRO-Mk3.0, GFDL-CM2.0 and UKMO-HADCM3) with two emission scenarios (A2 and B1) each. Based on a maximum entropy approach, models were trained on global data using a small set of uncorrelated predictors. The models were projected to the country of interest, using climate models that had been statistically downscaled to New Zealand, in order to obtain high resolution predictions. This study provides evidence of the potential range expansion of these species, with potentially suitable habitat increasing by as much as 169 % (A. cunninghamiana; with up to 115,805 km2 of suitable habitat), 133 % (P. guajava; 164,450 km2) and 208 % (S. actinophylla; 31,257 km2) by the end of the century compared to the currently suitable habitat. The results show that while predictions vary depending on the chosen climate scenario, there is remarkable consistency amongst most climate models within the same emission scenario, with overlaps in areas of predicted presence ranging between 81 % and 99.5 % (excluding CSIRO-Mk3.0). By having a better understanding of how climate change will affect distribution of invasive plants, appropriate management measures can be taken.

Supplementary material

10584_2012_605_MOESM1_ESM.pdf (9 kb)
ESM 1(PDF 9 kb)


  1. Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Model 162:211–232CrossRefGoogle Scholar
  2. Beaumont LJ, Gallagher RV, Downey PO, Thuiller W, Leishman MR, Hughes L (2009) Modelling the impact of Hieracium spp. on protected areas in Australia under future climates. Ecography 32:757–764CrossRefGoogle Scholar
  3. Bergengren JC, Thompson SL, Pollard D, DeConto RM (2001) Modeling global climate-vegetation interactions in a doubled CO2 world. Clim Chang 50:31–75CrossRefGoogle Scholar
  4. Bourdôt GW, Lamoureaux SL, Watt MS, Manning LK, Kriticos DJ (2012) The potential global distribution of the invasive weed Nassella neesiana under current and future climates. Biol Invasions 14:1545–1556CrossRefGoogle Scholar
  5. Bradley BA, Wilcove DS, Oppenheimer M (2010) Climate change increases risk of plant invasion in the Eastern United States. Biol Invasions 12:1855–1872CrossRefGoogle Scholar
  6. Broennimann O, Guisan A (2008) Predicting current and future biological invasions: both native and invaded ranges matter. Biol Lett 4:585–589CrossRefGoogle Scholar
  7. Cameron E (2000) Bangalow palm (Archontophoenix cunninghamiana) begins to naturalise. NZ Bot Soc Newsl 60:12–16Google Scholar
  8. Christianini AV (2006) Fecundity, dispersal and predation of seeds of Archontophoenix cunninghamiana H. Wendl. & Drude, an invasive palm in the Atlantic forest. Rev Bras Bot 29:587–594CrossRefGoogle Scholar
  9. Ebeling SK, Welk E, Auge H, Bruelheide H (2008) Predicting the spread of an invasive plant: combining experiments and ecological niche model. Ecography 31:709–719CrossRefGoogle Scholar
  10. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation and prediction across space and time. Ann Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  11. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  12. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342CrossRefGoogle Scholar
  13. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57CrossRefGoogle Scholar
  14. Hao W, Arora R, Yadav AK, Joshee N (2009) Freezing tolerance and cold acclimation in guava (Psidium guajava L.). Hortscience 44:1258–1266Google Scholar
  15. Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785CrossRefGoogle Scholar
  16. Hijmans RJ, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Global Chang Biol 12:2272–2281CrossRefGoogle Scholar
  17. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  18. Ibañez I, Silander JA Jr, Allen JM, Treanor SA, Wilson A (2009) Identifying hotspots for plant invasions and forecasting focal points of further spread. J Appl Ecol 46:1219–1228CrossRefGoogle Scholar
  19. IPCC (2000) Emission scenarios. A special report of IPCC working group IIIGoogle Scholar
  20. IPCC (2007) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  21. Jeschke JM, Strayer DL (2008) Usefulness of bioclimatic models for studying climate change and invasive species. Ann N Y Acad Sci 1134:1–24CrossRefGoogle Scholar
  22. Jones CC, Acker SA, Halpern CB (2010) Combining local- and large-scale models to predict the distributions of invasive plant species. Ecol Appl 20:311–326CrossRefGoogle Scholar
  23. Kriticos DJ, Yonow T, McFadyen RE (2005) The potential distribution of Chromolaena odorata (Siam weed) in relation to climate. Weed Res 45:246–254CrossRefGoogle Scholar
  24. Lee WG, Williams P, Cameron E (2000) Plant invasions in urban environments: the key to limiting new weeds in New Zealand. In: Suckling DM, Stevens PS (eds) Managing urban weeds and pests. Proceedings of a New Zealand Plant Protection Synopsium. New Zealand Plant Protection Society, LincolnGoogle Scholar
  25. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393CrossRefGoogle Scholar
  26. Lockwood JL, Hoopes MF, Marchetti MP (2007) Invasion ecology. Blackwell Publishing, OxfordGoogle Scholar
  27. Mack RN, Simberloff D, Lonsdale WM, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689–710CrossRefGoogle Scholar
  28. Ministry for the Environment (2008) Climate change effects and impacts assessment: a guidance manual for local government in New Zealand, 2nd edn. Ministry for the Environment, WellingtonGoogle Scholar
  29. Parker-Allie F, Musil CF, Thuiller W (2009) Effects of climate warming on the distributions of invasive Eurasian annual grasses: a South African perspective. Clim Chang 94:87–103CrossRefGoogle Scholar
  30. Pearce JL, Venier LA, Ferrier S, McKenney DW (2002) Measuring prediciton uncertainty in models of species distributions. In: Scott JM, Heglund PJ, Morrison ML et al (eds) Predicing species occurences—issues of accuracy and scale. Island Press, Washington, pp 383–390Google Scholar
  31. Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371CrossRefGoogle Scholar
  32. Peterson AT, Stewart A, Mohamed KI, Araújo MB (2008) Shifting global invasive potential of European plants with climate change. PLoS One 3:e2441CrossRefGoogle Scholar
  33. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  34. Reisinger A, Mullan B, Manning M, Wratt D, Nottage R (2010) Global and local climate change scenarios to support adaptation in New Zealand. In: Nottage R, Wratt D, Bornmann J, Jones K (eds) Climate change adaptation in New Zealand—future scenarios and some sectoral perspectives. New Zealand Climate Change Centre, Wellington, pp 26–43Google Scholar
  35. Rouget M, Richardson DM, Nel JL, Le Maitre DC, Egoh B, Mgidi T (2004) Mapping the potential ranges of major plant invaders in South Africa, Lesotho and Swaziland using climatic suitability. Divers Distrib 10:475–484CrossRefGoogle Scholar
  36. Scott JK, Batchelor KL (2006) Climate-based prediction of potential distributions of introduced Asparagus species in Australia. Plant Protect Q 21:91–98Google Scholar
  37. Sokolov AP, Stone PH, Forest CE et al (2009) Probabilistic forecast for 21st century climate based on uncertainties in emissions (without policy) and climate parameters. J Climate 22:5175–5204CrossRefGoogle Scholar
  38. Syphard AD, Franklin J (2009) Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography 32:907–918CrossRefGoogle Scholar
  39. Tait A, Henderson R, Turner R, Zheng X (2006) Thin plate smoothing spline interpolation of daily rainfall for New Zealand using a climatological rainfall surface. Int J Climatol 26:2097–2115CrossRefGoogle Scholar
  40. Thuiller W, Lavorel S, Araujo MB, Sykes MT, Prentice IC (2005a) Climate change threats to plant diversity in Europe. Proc Natl Acad Sci U S A 102:8245–8250CrossRefGoogle Scholar
  41. Thuiller W, Richardson DM, Pyssek P, Midgley GF, Hughes GO, Rouget M (2005b) Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Global Chang Biol 11:2234–2250CrossRefGoogle Scholar
  42. Thuiller W, Richardson DM, Midgley GF (2007) Will climate change promote alien plant invasions? Ecol Stud 193:197–211CrossRefGoogle Scholar
  43. Walther GR, Roques A, Hulme PE et al (2009) Alien species in a warmer world: risks and opportunities. Trends Ecol Evol 24:686–693CrossRefGoogle Scholar
  44. Watt MS, Kriticos DJ, Potter KJB, Manning LK, Tallent-Halsell N, Bourdôt GW (2010) Using species niche models to inform strategic management of weeds in a changing climate. Biol Invasions 12:3711–3725CrossRefGoogle Scholar
  45. Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA (2009) Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci U S A 106:19729–19736CrossRefGoogle Scholar
  46. Williams P, Kean J, Buxton R (2010) Multiple factors determine the rate of increase of an invading non-native tree in New Zealand. Biol Invasions 12:1377–1388CrossRefGoogle Scholar
  47. Wilson JRU, Richardson DM, Rouget M et al (2007) Residence time and potential range: crucial considerations in modelling plant invasions. Divers Distrib 13:11–22CrossRefGoogle Scholar
  48. Wilson PD, Downey PO, Leishman M, Gallagher R, Hughes L, O’Donnell J (2009) Weeds in a warmer world: predicting the impact of climate change on Australia’s alien plant species using MaxEnt. Plant Protect Q 24:84–87Google Scholar
  49. Woodward FI (1987) Climate and plant distribution. Cambridge University Press, CambridgeGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Centre for Biodiversity and Biosecurity, School of Biological SciencesThe University of AucklandAucklandNew Zealand

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