, Volume 39, Issue 1, pp 55–66 | Cite as

The Nativity and Distribution of the Cryptic Invader Phalaris arundinacea (Reed Canarygrass) in Riparian Areas of the Columbia and Missouri River Basins

  • Karin M. KettenringEmail author
  • Diane R. Menuz
  • Karen E. Mock
Applied Wetland Science


Cryptic invaders are inherently difficult to study due to morphological similarity with native lineages of the same species or genus. Wetland and riparian systems are particularly prone to plant invasions, and have been impacted by a number of widespread cryptic invaders such as Phalaris arundinacea (reed canarygrass). Here we combine molecular genetic analyses with species distribution modeling to assess the nativity of Phalaris and determine potential drivers of non-native Phalaris invasion in riparian areas across a large region of the semiarid northwestern USA. Based on our genetic analyses, we found that throughout our study region Phalaris is largely non-native, and no modern-day samples from our study region were of native North American origin. At least half of the four species distribution models suggested that non-native Phalaris invasion across the region was associated with warmer temperatures, more growing days, wetter summers, drier winters, higher nitrogen levels, shallower stream slopes, and at sites closer to roads and without a history of grazing. These findings can be used to determine the best locations for targeted monitoring. Furthermore, there is the potential for increased Phalaris invasion with climate change-induced temperature increases.


Climate change Dispersal Genetics Invasive species Nitrogen Species distribution modeling 



A. Jakubowski provided Phalaris samples and advice on running genetic analysis that was extremely important to this research. M. Barkworth at the Intermountain Herbarium and J. Solomon and M. Merello at the Missouri Botanical Garden provided access to specimens for sampling. M. Petru, J Raabova, and M. Duchacek provided herbarium samples from the Czech Republic. Thanks to J. Olson, R. Hill, C. Laine, R. Lokteff, A. Hill, P. Ebertowski, S. Galatowitsch, E. Archer, and especially B. Roper for data support and project feedback. We are also grateful to S. Bardot, who conducted the laboratory genetic analyses. Constructive advice from members of the Hawkins Lab at USU led to manuscript improvement. Funding was provided by the S.J. Quinney Masters Fellowship at Utah State University to DRM and the USDA Forest Service to KMK.

Supplementary material

13157_2018_1074_MOESM1_ESM.docx (23 kb)
Appendix S1 Details of model methods used (DOCX 23 kb)
13157_2018_1074_MOESM2_ESM.docx (115 kb)
Fig. S1 Summary of STRUCTURE HARVESTER results following STRUCTURE analysis of genetic data, using a burn-in period of 200,000 iterations and a sampling period of 400,000 iterations, with an admixture model (DOCX 115 kb)
13157_2018_1074_MOESM3_ESM.pdf (445 kb)
Fig. S2 Partial dependence plots for variables in the final random forest model of Phalaris arundinacea presence (PDF 444 kb)
13157_2018_1074_MOESM4_ESM.pdf (328 kb)
Fig. S3 Partial dependence plots for variables in the final boosted trees model of Phalaris arundinacea presence (PDF 328 kb)
13157_2018_1074_MOESM5_ESM.docx (13 kb)
Table S1 (DOCX 13 kb)


  1. Adams C, Galatowitsch S (2006) Increasing the effectiveness of reed canary grass (Phalaris arundinacea L.) control in wet meadow restorations. Restoration Ecology 14:441–451CrossRefGoogle Scholar
  2. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology 43:1223–1232CrossRefGoogle Scholar
  3. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22:42–47CrossRefGoogle Scholar
  4. Ashworth S (1997) Comparison between restored and reference sedge meadow wetlands in south-Central Wisconsin. Wetlands 17:518–527CrossRefGoogle Scholar
  5. Baker DB, Richards RP, Loftus TT, Kramer JW (2004) A new flashiness index: characteristics and applications to Midwestern rivers and streams. JAWRA Journal of the American Water Resources Association 40:503–522CrossRefGoogle Scholar
  6. Carlton JT (1996) Biological invasions and cryptogenic species. Ecology 77:1653–1655CrossRefGoogle Scholar
  7. Casler M, Phillips M, Krohn A (2009) DNA polymorphisms reveal geographic races of reed canarygrass. Crop Sci 49:2139–2148Google Scholar
  8. Ciotir C, Kirk H, Row JR, Freeland JR (2013) Intercontinental dispersal of Typha angustifolia and T. latifolia between Europe and North America has implications for Typha invasions. Biol Invasions15:1377–1390Google Scholar
  9. Coops H, Van der Velde G (1995) Seed dispersal, germination and seedling growth of six helophyte species in relation to water-level zonation. Freshwater Biology 34:13–20CrossRefGoogle Scholar
  10. Cutler D, Edwards T Jr, Beard K, Cutler A, Hess K, Gibson J, Lawler J (2007) Random forests for classification in ecology. Ecology 88:2783–2792CrossRefGoogle Scholar
  11. Daly C, Halbleib M, Smith JI, Gibson WP, Doggett MK, Taylor GH, Curtis J, Pasteris PP (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology 28:2031–2064CrossRefGoogle Scholar
  12. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing ViennaGoogle Scholar
  13. Earl DA (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361Google Scholar
  14. Eidenshink J, Schwind B, Brewer K, Zhu Z, Quayle B, Howard S (2007) A project for monitoring trends in burn severity. Fire Ecology 3:3–21CrossRefGoogle Scholar
  15. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14:2611–2620CrossRefGoogle Scholar
  16. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587Google Scholar
  17. Fenn ME, Baron JS, Allen EB, Rueth HM, Nydick KR, Geiser L, Bowman WD, Sickman JO, Meixner T, Johnson DW (2003) Ecological effects of nitrogen deposition in the western United States. BioScience 53:404–420CrossRefGoogle Scholar
  18. Fielding A, Bell J (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38–49CrossRefGoogle Scholar
  19. Freeman EA, Moisen GG (2008) A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecological Modelling 217:48–58CrossRefGoogle Scholar
  20. Galatowitsch S, Anderson N, Ascher P (1999) Invasiveness in wetland plants in temperate North America. Wetlands 19:733–755CrossRefGoogle Scholar
  21. Gallien L, Douzet R, Pratte S, Zimmermann NE, Thuiller W (2012) Invasive species distribution models–how violating the equilibrium assumption can create new insights. Global Ecology and Biogeography 21:1126–1136CrossRefGoogle Scholar
  22. Gebauer AD, Brown R, Schwab S, Nezat C, McNeely C (2016) Effects of an invasive grass (Phalaris arundinacea) on water availability in semi-arid riparian zones. Wetlands 36:59–72CrossRefGoogle Scholar
  23. Gesch D, Evans G, Mauck J, Hutchinson J, Carswell Jr. WJ (2009) The National Map—Elevation:U.S. Geological Survey Fact Sheet 2009–3053Google Scholar
  24. Green EK, Galatowitsch SM (2002) Effects of Phalaris arundinacea and nitrate-N addition on the establishment of wetland plant communities. Journal of Applied Ecology 39:134–144CrossRefGoogle Scholar
  25. Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007) What matters for predicting the occurrences of trees: techniques, data or species' characteristics? Ecological Monographs 77:615–630CrossRefGoogle Scholar
  26. Hansen J, Castelle A (1999) Insect diversity in soils of tidal and non-tidal wetlands of Spencer Island, Washington. Journal of the Kansas Entomological Society:262–272Google Scholar
  27. Healy MT, Rojas IM, Zedler JB (2015) Adaptive control of Phalaris arundinacea in Curtis Prairie. Invasive Plant Science and Management 8:363–373CrossRefGoogle Scholar
  28. Henderson RC, Archer EK, Bouwes BA, Coles-Ritchie MS, Kershner JL (2005) PACFISH/INFISH biological opinion (PIBO): effectiveness monitoring program seven-year status report 1998 through 2004, general technical report RMRS-GTR-162. USDA Forest Service Rocky Mountain Research Station, Fort Colllins, COGoogle Scholar
  29. Herr-Turoff A, Zedler JB (2007) Does morphological plasticity of the Phalaris arundinacea canopy increase invasiveness? Plant Ecology 193:265–277CrossRefGoogle Scholar
  30. Hillhouse H, Tunnell S, Stubbendieck J (2010) Spring grazing impacts on the vegetation of reed canarygrass-invaded wetlands. Rangeland Ecology & Management 63:581–587CrossRefGoogle Scholar
  31. Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied logistic regression. John Wiley & SonsGoogle Scholar
  32. Iannone BV, Galatowitsch SM (2008) Altering light and soil N to limit Phalaris arundinacea reinvasion in sedge meadow restorations. Restoration Ecology 16:689–701CrossRefGoogle Scholar
  33. Jakubowski AR, Casler MD, Jackson RD (2010) Landscape context predicts reed canarygrass invasion: implications for management. Wetlands 30:685–692CrossRefGoogle Scholar
  34. Jakubowski AR, Casler MD, Jackson RD (2011) Has selection for improved agronomic traits made reed canarygrass invasive? PloS One 6:e25757Google Scholar
  35. Jakubowski AR, Casler MD, Jackson RD (2013) Genetic evidence suggests a widespread distribution of native North American populations of reed canarygrass. Biol Invasions 15:261–268Google Scholar
  36. Jakubowski AR, Jackson RD, Casler MD (2014) The history of reed canarygrass in North America: persistence of natives among invading Eurasian populations. Crop Science 54:210–219CrossRefGoogle Scholar
  37. Kercher SM, Zedler JB (2004) Multiple disturbances accelerate invasion of reed canary grass (Phalaris arundinacea L.) in a mesocosm study. Oecologia 138:455–464CrossRefGoogle Scholar
  38. Kettenring KM, de Blois S, Hauber DP (2012) Moving from a regional to a continental perspective of Phragmites australis invasion in North America. AoB Plants 2012:pls040Google Scholar
  39. Kidd S, Yeakley J (2015) Riparian wetland plant response to livestock exclusion in the lower Columbia River Basin. Natural Areas Journal 35:504–514CrossRefGoogle Scholar
  40. Klebesadel LJ, Dofing SM (1991) Reed canarygrass in Alaska: influence of latitude-of-adaptation on winter survival and forage productivity, and observations on seed production. Alaska Agricultural and Forestry Experiment Station Bulletin 84:1–26Google Scholar
  41. Lavergne S, Molofsky J (2004) Reed canary grass (Phalaris arundinacea) as a biological model in the study of plant invasions. Critical Reviews in Plant Sciences 23:415–429CrossRefGoogle Scholar
  42. Lavergne S, Molofsky J (2007) Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proceedings of the National Academy of Sciences 104:3883–3888CrossRefGoogle Scholar
  43. Lavoie C, Dufresne C, Delisle F (2005) The spread of reed canarygrass (Phalaris arundinacea) in Québec: a spatio-temporal perspective. Ecoscience 12:366–375CrossRefGoogle Scholar
  44. Leck MA, Parker VT, Simpson RL (2008) Seedling ecology and evolution. Cambridge University PressGoogle Scholar
  45. Lindig-Cisneros R, Zedler J (2001) Effect of light on seed germination in Phalaris arundinacea L. (reed canary grass). Plant Ecol 155:75–78Google Scholar
  46. Lindig-Cisneros R, Zedler JB (2002) Phalaris arundinacea seedling establishment: effects of canopy complexity in fen, mesocosm, and restoration experiments. Canadian Journal of Botany 80:617–624CrossRefGoogle Scholar
  47. Magee TK, Ernst TL, Kentula ME, Dwire KA (1999) Floristic comparison of freshwater wetlands in an urbanizing environment. Wetlands 19:517–534CrossRefGoogle Scholar
  48. Marlor KM, Webster CR, Chimner RA (2014) Disturbance and wetland type alter reed canarygrass cover in northern Michigan. Invasive Plant Science and Management 7:121–131CrossRefGoogle Scholar
  49. Martinez AE, McDowell PF (2016) Invasive reed canarygrass (Phalaris arundinacea) and native vegetation channel roughness. Invasive Plant Science and Management 9:12–21CrossRefGoogle Scholar
  50. Matthews JW, Peralta AL, Soni A, Baldwin P, Kent AD, Endress AG (2009) Local and landscape correlates of non-native species invasion in restored wetlands. Ecography 32:1031–1039CrossRefGoogle Scholar
  51. Menuz DR, Kettenring KM, Hawkins CP, Cutler DR (2015) Non-equilibrium in plant distribution models–only an issue for introduced or dispersal limited species? Ecography 38:231–240CrossRefGoogle Scholar
  52. Meredith C, Archer E, Scully R, Van Wagenen A, Ojala J, Hough-Snee N, Roper B (2011) PIBO effectiveness monitoring program for streams and riparian areas: 2011 annual summary report. USDA Forest Service, prepared by the PACFISH INFISH Biological Opinion Effectiveness Monitoring Staff:42ppGoogle Scholar
  53. Moody ML, Palomino N, Weyl PSR, Coetzee JA, Newman RM, Harms NE, Liu X, Thum RA (2016) Unraveling the biogeographic origins of the Eurasian watermilfoil (Myriophyllum spicatum) invasion in North America. American Journal of Botany 103:709–718CrossRefGoogle Scholar
  54. Nelson MF, Anderson NO, Casler MD, Jakubowski AR (2014) Population genetic structure of N. American and European Phalaris arundinacea L. as inferred from inter-simple sequence repeat markers. Biological Invasions 16:353–363CrossRefGoogle Scholar
  55. Olson JR, Hawkins CP (2012) Predicting natural base-flow stream water chemistry in the western United States. Water Resources Research 48Google Scholar
  56. Paine LK, Ribic CA (2002) Comparison of riparian plant communities under four land management systems in southwestern Wisconsin. Agriculture, Ecosystems & Environment 92:93–105CrossRefGoogle Scholar
  57. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6:288–295Google Scholar
  58. Perry LG, Galatowitsch SM (2004) The influence of light availability on competition between Phalaris arundinacea and a native wetland sedge. Plant Ecology 170:73–81CrossRefGoogle Scholar
  59. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959Google Scholar
  60. Ratner B (2009) The correlation coefficient: its values range between +1/−1, or do they? Journal of Targeting, Measurement and Analysis for Marketing 17:139–142CrossRefGoogle Scholar
  61. Ringold P, Magee T, Peck D (2008) Twelve invasive plant taxa in US western riparian ecosystems. Journal of the North American Benthological Society 27:949–966CrossRefGoogle Scholar
  62. Roman J, Darling JA (2007) Paradox lost: genetic diversity and the success of aquatic invasions. Trends Ecol Evol 22:454–464Google Scholar
  63. Rood SB, Braatne JH, Goater LA (2010) Favorable fragmentation: river reservoirs can impede downstream expansion of riparian weeds. Ecological Applications 20:1664–1677CrossRefGoogle Scholar
  64. Sahramaa M, Jauhiainen L (2003) Characterization of development and stem elongation of reed canary grass under northern conditions. Industrial Crops and Products 18:155–169CrossRefGoogle Scholar
  65. Saltonstall K (2002) Cryptic invasion by a non-native genotype of the common reed, Phragmites australis, into North America. Proceedings of the National Academy of Sciences 99:2445–2449CrossRefGoogle Scholar
  66. Saltonstall K (2003) A rapid method for identifying the origin of North American Phragmites populations using RFLP analysis. Wetlands 23:1043–1047Google Scholar
  67. Schooler SS, McEvoy PB, Coombs EM (2006) Negative per capita effects of purple loosestrife and reed canary grass on plant diversity of wetland communities. Diversity and Distributions 12:351–363CrossRefGoogle Scholar
  68. Schwede DB, Dennis RL, Bitz MA (2009) The watershed deposition tool: a tool for incorporating atmospheric deposition in water quality analyses. Journal of the American Water Resources Association 45:973–985CrossRefGoogle Scholar
  69. Segurado P, Araujo M (2004) An evaluation of methods for modelling species distributions. Journal of Biogeography 31:1555–1568CrossRefGoogle Scholar
  70. Soil Survey Staff U.S. General Soil Map (STATSGO2) (n.d.) In NRCS USDA (ed.)Google Scholar
  71. Spyreas G, Wilm BW, Plocher AE, Ketzner DM, Matthews JW, Ellis JL, Heske EJ (eds) (2010) Biological consequences of invasion by reed canary grass (Phalaris arundinacea). Biological Invasions 12:1253–1267Google Scholar
  72. U.S. Census Bureau (2009) U.S. Census 2000 TIGER/Line files machine-readable data files. Washington, D.C.Google Scholar
  73. Václavík T, Meentemeyer RK (2009) Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions? Ecol Model 220:3248–3258Google Scholar
  74. Voshell SM, Hilu KW (2014) Canary grasses (Phalaris, Poaceae): biogeography, molecular dating and the role of floret structure in dispersal. Molecular Ecology 23:212–224CrossRefGoogle Scholar
  75. website 1 (n.d.) The PLANTS Database. In United States Department of Agriculture—Natural Resources Conservation Service (ed.), National Plant Data Team, Greensboro, NC 27401–4901 USAGoogle Scholar
  76. website 2 (n.d.) Global Invasive Species Database. In Invasive Species Specialist Group (ISSG) of the IUCN Species Survival Commission (ed.)Google Scholar
  77. website 3 (2014) Geospatial Modelling Environment. In H. L. Beyer (ed.)Google Scholar
  78. Werner K, Zedler J (2002) How sedge meadow soils, microtopography, and vegetation respond to sedimentation. Wetlands 22:451–466Google Scholar
  79. Wolock DM (2003) Base-flow index grid for the conterminous United States. U.S. Geological Survey. Reston, VirginiaGoogle Scholar
  80. Zedler JB, Kercher S (2004) Causes and consequences of invasive plants in wetlands: opportunities, opportunists, and outcomes. Critical Reviews in Plant Sciences 23:431–452CrossRefGoogle Scholar

Copyright information

© Society of Wetland Scientists 2018

Authors and Affiliations

  1. 1.Ecology Center and Department of Watershed SciencesUtah State UniversityLoganUSA
  2. 2.Utah Geological SurveySalt Lake CityUSA
  3. 3.Ecology Center and Department of Wildland ResourcesUtah State UniversityLoganUSA

Personalised recommendations