Biological Invasions

, Volume 19, Issue 7, pp 2159–2170 | Cite as

Present and future distribution of three aquatic plants taxa across the world: decrease in native and increase in invasive ranges

  • Morgane GillardEmail author
  • Gabrielle Thiébaut
  • Carole Deleu
  • Boris Leroy
Original Paper


Inland aquatic ecosystems are vulnerable to both climate change and biological invasion at broad spatial scales. The aim of this study was to establish the current and future potential distribution of three invasive plant taxa, Egeria densa, Myriophyllum aquaticum and Ludwigia spp., in their native and exotic ranges. We used species distribution models (SDMs), with nine different algorithms and three global circulation models, and we restricted the suitability maps to cells containing aquatic ecosystems. The current bioclimatic range of the taxa was predicted to represent 6.6–12.3% of their suitable habitats at global scale, with a lot of variations between continents. In Europe and North America, their invasive ranges are predicted to increase up to two fold by 2070 with the highest gas emission scenario. Suitable new areas will mainly be located to the north of their current range. In other continents where they are exotic and in their native range (South America), the surface areas of suitable locations are predicted to decrease with climate change, especially for Ludwigia spp. in South America (down to −55% by 2070 with RCP 8.5 scenario). This study allows to identify areas vulnerable to ongoing invasions by aquatic plant species and thus could help the prioritisation of monitoring and management, as well as contribute to the public awareness regarding biological invasions.


Brazilian waterweed Climate change Parrot feather RCP scenarios Species distribution models Water primroses 



We kindly thank Márcio José Silveira for providing the occurrences of the studied taxa in Brazil, and Aldyth Nys for the English editing of the manuscript. This work was supported by a Ph.D. fellowship from the French Ministry for Higher Education and Research to MG. We would like to warmly thank the two reviewers who evaluated and contributed to improve a previous version of this manuscript.

Supplementary material

10530_2017_1428_MOESM1_ESM.pdf (3.3 mb)
Supplementary material 1 (PDF 3402 kb)


  1. Alahuhta J, Heino J, Luoto M (2011) Climate change and the future distributions of aquatic macrophytes across boreal catchments. J Biogeogr 38:383–393. doi: 10.1111/j.1365-2699.2010.02412.x CrossRefGoogle Scholar
  2. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47. doi: 10.1016/j.tree.2006.09.010 CrossRefPubMedGoogle Scholar
  3. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol Evol 3:327–338. doi: 10.1111/j.2041-210X.2011.00172.x CrossRefGoogle Scholar
  4. Bellard C, Thuiller W, Leroy B et al (2013) Will climate change promote future invasions? Glob Chang Biol 19:3740–3748. doi: 10.1111/gcb.12344 CrossRefPubMedPubMedCentralGoogle Scholar
  5. Bellard C, Leroy B, Thuiller W et al (2016) Major drivers of invasion risks throughout the world. Ecosphere 7:1–14. doi: 10.1002/ecs2.1241 CrossRefGoogle Scholar
  6. Bornette G, Puijalon S (2010) Response of aquatic plants to abiotic factors: a review. Aquat Sci 73:1–14. doi: 10.1007/s00027-010-0162-7 CrossRefGoogle Scholar
  7. Bradshaw CJA, Leroy B, Bellard C et al (2016) Massive yet grossly underestimated costs of invasive insects. Nat Commun. doi: 10.1038/ncomms12986 Google Scholar
  8. Breiman L (2001) Random forests. Mach Learn 45:5–32. doi: 10.1023/A:1010933404324 CrossRefGoogle Scholar
  9. Breiman L, Friedman JH, Olshean RA, Stone CJ (1984) Classification and regression trees. Chapman and Hall, LondonGoogle Scholar
  10. Broennimann O, Guisan A (2008) Predicting current and future biological invasions: both native and invaded ranges matter. Biol Lett 4:585–589. doi: 10.1098/rsbl.2008.0254 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Broennimann O, Treier UA, Müller-Schärer H et al (2007) Evidence of climatic niche shift during biological invasion. Ecol Lett 10:701–709. doi: 10.1111/j.1461-0248.2007.01060.x CrossRefPubMedGoogle Scholar
  12. Buisson L, Thuiller W, Casajus N et al (2010) Uncertainty in ensemble forecasting of species distribution. Glob Chang Biol 16:1145–1157. doi: 10.1111/j.1365-2486.2009.02000.x CrossRefGoogle Scholar
  13. Carey MP, Sethi SA, Larsen SJ, Rich CF (2016) A primer on potential impacts, management priorities, and future directions for Elodea spp. in high latitude systems: learning from the Alaskan experience. Hydrobiologia 777:1–19. doi: 10.1007/s10750-016-2767-x CrossRefGoogle Scholar
  14. Collins M, Tett SFB, Cooper C (2001) The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 17:61–81. doi: 10.1007/s003820000094 CrossRefGoogle Scholar
  15. Dandelot S, Verlaque R, Dutartre A, Cazaubon A (2005) Ecological, dynamic and taxonomic problems due to Ludwigia (Onagraceae) in France. Hydrobiologia 551:131–136. doi: 10.1007/s10750-005-4455-0 CrossRefGoogle Scholar
  16. Dudgeon D, Arthington AH, Gessner MO et al (2006) Freshwater biodiversity: importance, threats, status and conservation challenges. Biol Rev 81:163–182. doi: 10.1017/S1464793105006950 CrossRefPubMedGoogle Scholar
  17. Ebeling SK, Welk E, Auge H, Bruelheide H (2008) Predicting the spread of an invasive plant: combining experiments and ecological niche model. Ecography (Cop) 31:709–719. doi: 10.1111/j.1600-0587.2008.05470.x CrossRefGoogle Scholar
  18. Elith J, Ferrier S, Huettmann F, Leathwick J (2005) The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. Ecol Model 186:280–289. doi: 10.1016/j.ecolmodel.2004.12.007 CrossRefGoogle Scholar
  19. Feijoó C, García ME, Momo F, Toja J (2002) Nutrient absorption by the submerged macrophyte Egeria densa Planch.: effect of ammonium and phosphorus availability in the water colum on growth and nutrient uptake. Limnetica 21:93–104Google Scholar
  20. Finch JM, Samways MJ, Hill TR et al (2006) Application of predictive distribution modelling to invertebrates: Odonata in South Africa. Biodivers Conserv 15:4239–4251. doi: 10.1007/s10531-005-3577-z CrossRefGoogle Scholar
  21. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67CrossRefGoogle Scholar
  22. Gallardo B, Aldridge DC (2013) The “dirty dozen”: socio-economic factors amplify the invasion potential of 12 high-risk aquatic invasive species in Great Britain and Ireland. J Appl Ecol 50:757–766. doi: 10.1111/1365-2664.12079 CrossRefGoogle Scholar
  23. Gallien L, Douzet R, Pratte S et al (2012) Invasive species distribution models—how violating the equilibrium. Glob Ecol Biogeogr 21:1126–1136. doi: 10.1111/j.1466-8238.2012.00768.x CrossRefGoogle Scholar
  24. Gent PR, Danabasoglu G, Donner LJ et al (2011) The community climate system model version 4. J Clim 24:4973–4991. doi: 10.1175/2011JCLI4083.1 CrossRefGoogle Scholar
  25. Getsinger KD, Dillon CR (1984) Quiescence, growth and senescence of Egeria densa in Lake Marion. Aquat Bot 20:329–338. doi: 10.1016/0304-3770(84)90096-2 CrossRefGoogle Scholar
  26. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009. doi: 10.1111/j.1461-0248.2005.00792.x CrossRefGoogle Scholar
  27. Guisan A, Tingley R, Baumgartner JB et al (2013) Predicting species distributions for conservation decisions. Ecol Lett 16:1424–1435. doi: 10.1111/ele.12189 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Hastie T, Tibshirani R (1990) Generalized additive models. Chapman and Hall, LondonGoogle Scholar
  29. Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89:1255–1270. doi: 10.2307/2290989 CrossRefGoogle Scholar
  30. Heikkinen R, Leikola N, Fronzek S et al (2009) Predicting distribution patterns and recent northward range shift of an invasive aquatic plant: Elodea canadensis in Europe. BioRisk 2:1–32. doi: 10.3897/biorisk.2.4 CrossRefGoogle Scholar
  31. Hijmans RJ, Cameron SE, Parra JL et al (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. doi: 10.1002/joc.1276 CrossRefGoogle Scholar
  32. Hussner A (2010) Growth response and root system development of the invasive Ludwigia grandiflora and Ludwigia peploides to nutrient availability and water level. Fundam Appl Limnol/Arch Hydrobiol 177:189–196. doi: 10.1127/1863-9135/2010/0177-0189 CrossRefGoogle Scholar
  33. Hussner A (2012) Alien aquatic plant species in European countries. Weed Res 52:297–306. doi: 10.1111/j.1365-3180.2012.00926.x CrossRefGoogle Scholar
  34. Hussner A, Champion PD (2011) Myriophyllum aquaticum (Vell.) Verdcourt (parrot feather). In: Francis RA (ed) A Handbook of global freshwater invasive species. Routledge, New York, p 456Google Scholar
  35. Hussner A, Meyer C, Busch J (2009) The influence of water level and nutrient availability on growth and root system development of Myriophyllum aquaticum. Weed Res 49:73–80CrossRefGoogle Scholar
  36. IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, CambridgeGoogle Scholar
  37. Jaccard P (1901) Distribution de la flore alpine dans le Bassin des Drouces et dans quelques regions voisines. Bull la Société Vaudoise des Sci Nat 37:241–272Google Scholar
  38. Kelly R, Leach K, Cameron A et al (2014) Combining global climate and regional landscape models to improve prediction of invasion risk. Divers Distrib 20:884–894. doi: 10.1111/ddi.12194 CrossRefGoogle Scholar
  39. Kriticos DJ, Sutherst RW, Brown JR et al (2003) Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia. J Appl Ecol 40:111–124CrossRefGoogle Scholar
  40. Lehner B, Döll P (2004) Development and validation of a global database of lakes, reservoirs and wetlands. J Hydrol 296:1–22. doi: 10.1016/j.jhydrol.2004.03.028 CrossRefGoogle Scholar
  41. Leroy B, Paschetta M, Canard A et al (2013) First assessment of effects of global change on threatened spiders: potential impacts on Dolomedes plantarius (Clerck) and its conservation plans. Biol Conserv 161:155–163. doi: 10.1016/j.biocon.2013.03.022 CrossRefGoogle Scholar
  42. Li W (2014) Environmental opportunities and constraints in the reproduction and dispersal of aquatic plants. Aquat Bot 118:62–70. doi: 10.1016/j.aquabot.2014.07.008 CrossRefGoogle Scholar
  43. Li W, Guo Q (2013) How to assess the prediction accuracy of species presence–absence models without absence data? Ecography (Cop) 36:788–799. doi: 10.1111/j.1600-0587.2013.07585.x CrossRefGoogle Scholar
  44. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography (Cop) 28:385–393. doi: 10.1111/j.0906-7590.2005.03957.x CrossRefGoogle Scholar
  45. Lowe S, Browne M, Boudjelas S, De Poorter M (2004) 100 of the world’s worst invasive alien species a selection from the Global Invasive Species Database. Invasive Species Specialist Group, Species Survival Commission, World Conservation Union (IUCN)Google Scholar
  46. Mainka SA, Howard GW (2010) Climate change and invasive species: double jeopardy. Integr Zool 5:102–111. doi: 10.1111/j.1749-4877.2010.00193.x CrossRefPubMedGoogle Scholar
  47. McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman and Hall, LondonCrossRefGoogle Scholar
  48. Peterson AT, Papes M, Kluza DA (2003) Predicting the potential invasive distributions of four alien plant species in North America. Weed Sci 51:863–868CrossRefGoogle Scholar
  49. Peterson AT, Stewart A, Mohamed KI, Araújo MB (2008) Shifting global invasive potential of European plants with climate change. PLoS ONE. doi: 10.1371/journal.pone.0002441 Google Scholar
  50. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. doi: 10.1016/j.ecolmodel.2005.03.026 CrossRefGoogle Scholar
  51. Qin Z, DiTommaso A, Wu RS, Huang HY (2014) Potential distribution of two Ambrosia species in China under projected climate change. Weed Res 54:520–531. doi: 10.1111/wre.12100 CrossRefGoogle Scholar
  52. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  53. Ridgeway G (1999) The state of boosting. Comput Sci Stat 31:172–181Google Scholar
  54. Ripley BD (1996) Neural networks and pattern recognition. Cambridge University, CambridgeCrossRefGoogle Scholar
  55. Ruaux B, Greulich S, Haury J, Berton J-P (2009) Sexual reproduction of two alien invasive Ludwigia (Onagraceae) on the middle Loire River, France. Aquat Bot 90:143–148. doi: 10.1016/j.aquabot.2008.08.003 CrossRefGoogle Scholar
  56. Santamaría L (2002) Why are most aquatic plants widely distributed? Dispersal, clonal growth and small-scale heterogeneity in a stressful environment. Acta Oecol 23:137–154. doi: 10.1016/S1146-609X(02)01146-3 CrossRefGoogle Scholar
  57. Stephens PA, Mason LR, Green RE et al (2016) Consistent response of bird populations to climate change on two continents. Science (80-) 352:84–87. doi: 10.1126/science.aac4858 CrossRefGoogle Scholar
  58. Stiers I, Crohain N, Josens G, Triest L (2011) Impact of three aquatic invasive species on native plants and macroinvertebrates in temperate ponds. Biol Invasions 13:2715–2726. doi: 10.1007/s10530-011-9942-9 CrossRefGoogle Scholar
  59. Thalmann DJK, Kikodze D, Khutsishvili M et al (2015) Areas of high conservation value in Georgia: present and future threats by invasive alien plants. Biol Invasions 17:1041–1054. doi: 10.1007/s10530-014-0774-2 CrossRefGoogle Scholar
  60. Thouvenot L, Haury J, Thiebaut G (2013a) A success story: water primroses, aquatic plant pests. Aquat Conserv Mar Freshw Ecosyst 23:790–803Google Scholar
  61. Thouvenot L, Puech C, Martinez L et al (2013b) Strategies of the invasive macrophyte Ludwigia grandiflora in its introduced range: competition, facilitation or coexistence with native and exotic species? Aquat Bot 107:8–16. doi: 10.1016/j.aquabot.2013.01.003 CrossRefGoogle Scholar
  62. Thuiller W, Lafourcade B, Engler R, Araújo MB (2009) BIOMOD—a platform for ensemble forecasting of species distributions. Ecography (Cop) 32:369–373. doi: 10.1111/j.1600-0587.2008.05742.x CrossRefGoogle Scholar
  63. Watts G, Battarbee RW, Bloomfield JP et al (2015) Climate change and water in the UK—past changes and future prospects. Prog Phys Geogr 39:6–28. doi: 10.1177/0309133314542957 CrossRefGoogle Scholar
  64. Whitehead PG, Wilby RL, Battarbee RW et al (2009) A review of the potential impacts of climate change on surface water quality. Hydrol Sci J 54:101–123. doi: 10.1623/hysj.54.1.101 CrossRefGoogle Scholar
  65. Yarrow M, Marin VH, Finlayson M et al (2009) The ecology of Egeria densa Planchon (Liliopsida: Alismatales): a wetland ecosystem engineer? Rev Chil Hist Nat 82:299–313CrossRefGoogle Scholar
  66. Yukimoto S, Adachi Y, Hosaka M et al (2012) A new global climate model of the Meteorological Research Institute: MRI-CGCM3. J Meteorol Soc Jpn 90A:23–64. doi: 10.2151/jmsj.2012-A02 CrossRefGoogle Scholar
  67. Zhang C, Boyle KJ (2010) The effect of an aquatic invasive species (Eurasian watermilfoil) on lakefront property values. Ecol Econ 70:394–404. doi: 10.1016/j.ecolecon.2010.09.011 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.ECOBIO, UMR 6553 CNRSUniversité de Rennes 1RennesFrance
  2. 2.IGEPP, UMR 1349 INRAUniversité de Rennes 1Le RheuFrance
  3. 3.BOREA, UMR 7208, Muséum National d’Histoire Naturelle, Université Pierre et Marie Curie, Université de Caen Basse-Normandie, CNRS, IRDSorbonne UniversitéParisFrance

Personalised recommendations