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The ecological niche and reciprocal prediction of the disjunct distribution of an invasive species: the example of Ailanthus altissima

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Abstract

Knowledge of the ecological niches of invasive species in native and introduced ranges can inform management as well as ecological and evolutionary theory. Here, we identified and compared factors associated with the distribution of an invasive tree, Ailanthus altissima, in both its native Chinese and introduced US ranges and predicted potential US distribution. For both ranges separately, we selected suites of the most parsimonious logistic regression models of occurrence based on environmental variables and evaluated these against independent data. We then incorporated information from both ranges in a simple Bayesian model to predict the potential US distribution. Occurrence of A. altissima in both ranges exhibited a unimodal response to temperature variables. In China, occurrence had negative relationships with topographic wetness and forest cover and positive relationships with precipitation and agricultural and urban land use. In the US, A. altissima was associated with intermediate levels of forest cover and precipitation. The Bayesian model identified 58–80% of 10-arc minute grid cells in the conterminous US as containing suitable areas for A. altissima. The best model developed from Chinese data applied to the US matched most areas of observed occurrence but under-predicted occurrence in lower probability areas. This discrepancy is suggestive of a broadening of the ecological niche of A. altissima and may be due to such factors as less intense competition, increased potency of allelopathy, and novel genotypes formed from multiple introductions. The Bayesian model suggests that A. altissima has the potential to substantially expand its distribution in the US.

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References

  • Akaike H (1974) New look at statistical-model identification. IEEE Trans Automat Contr AC 19:716–723

    Article  Google Scholar 

  • Albright TP, Anderson DP, Keuler NS, Pearson SM, Turner MG (2009) The spatial legacy of introduction: Celastrus orbiculatus in the southern Appalachians. J Appl Ecol (in press)

  • Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Modell 162:211–232

    Article  Google Scholar 

  • Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Modell 157:101–118

    Article  Google Scholar 

  • Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Modell 200:1–19

    Article  Google Scholar 

  • Blossey B, Notzold R (1995) Evolution of increased competitive ability in invasive nonindigenous plants—a hypothesis. J Ecol 83:887–889

    Article  Google Scholar 

  • Boyce MS, Vernier PR, Nielsen SE, Schmiegelow FKA (2002) Evaluating resource selection functions. Ecol Modell 157:281–300

    Article  Google Scholar 

  • Brown JH, Lomolino MV (1998) Biogeography. Sinauer Associates, Sunderland, p 691

    Google Scholar 

  • Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York, p 488

    Google Scholar 

  • Callaway RM, Aschehoug ET (2000) Invasive plants versus their new and old neighbors: a mechanism for exotic invasion. Science 290:521–523

    Article  CAS  PubMed  Google Scholar 

  • Chen H, Chen LJ, Albright TP (2007) Predicting the potential distribution of invasive exotic species using GIS and information-theoretic approaches: a case of ragweed (Ambrosia artemisiifolia L.) distribution in China. Chin Sci Bull 52:1223–1230

    Article  Google Scholar 

  • Clark JS (2005) Why environmental scientists are becoming Bayesians. Ecol Lett 8:2–14

    Article  Google Scholar 

  • Cohen JE (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:37–46

    Article  Google Scholar 

  • Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289:2068–2074

    Article  CAS  PubMed  Google Scholar 

  • Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151

    Article  Google Scholar 

  • Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49

    Article  Google Scholar 

  • Gray A (1846) Analogy between the flora of Japan and that of the United States. Am J Sci Arts II 2:135–136

    Google Scholar 

  • Greer G, Aldrich PR (2005) Genetics and biochemical variation of US Ailanthus altissima populations: a preliminary discussion of a research plan. 16th US Department of Agriculture interagency research forum on gypsy moth and other invasive species. USDA Forest Service, Northeastern Research Station, Annapolis

    Google Scholar 

  • Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009

    Article  Google Scholar 

  • Guo Q (1999) Ecological comparisons between Eastern Asia and North America: historical and geographical perspectives. J Biogeogr 26:199–206

    Article  Google Scholar 

  • Heisey RM, Heisey TK (2003) Herbicidal effects under field conditions of Ailanthus altissima bark extract, which contains ailanthone. Plant Soil 256:85–99

    Article  CAS  Google Scholar 

  • Hierro JL, Maron JL, Callaway RM (2005) A biogeographical approach to plant invasions: the importance of studying exotics in their introduced and native range. J Ecol 93:5–15

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York, p 373

    Book  Google Scholar 

  • Houghton RA, Hackler JL, Lawrence KT (1999) The US carbon budget: contributions from land-use change. Science 285:574–578

    Article  CAS  PubMed  Google Scholar 

  • Hutchinson GE (1957) Concluding remarks. Population studies: animal ecology and demography. Cold Spring Harb Symp Quant Biol 22:415–427

    Google Scholar 

  • Jin MH, Yook J, Lee E, Lin CX, Quan Z, Son KH, Bae KH, Kim HP, Kang SS, Chang HW (2006) Anti-inflammatory activity of Ailanthus altissima in ovalbumin-induced lung inflammation. Biol Pharm Bull 29:884–888

    Article  CAS  PubMed  Google Scholar 

  • Johnson JB, Omland KS (2004) Model selection in ecology and evolution. Trends Ecol Evol 19:101–108

    Article  PubMed  Google Scholar 

  • Johnson CJ, Nielsen SE, Merrill EH, McDonald TL, Boyce MS (2006) Resource selection functions based on use-availability data: theoretical motivation and evaluation methods. J Wildl Manage 70:347–357

    Article  Google Scholar 

  • Kadmon R, Farber O, Danin A (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413

    Article  Google Scholar 

  • Keane RM, Crawley MJ (2002) Exotic plant invasions and the enemy release hypothesis. Trends Ecol Evol 17:164–170

    Article  Google Scholar 

  • Knapp LB, Canham CD (2000) Invasion of an old-growth forest in New York by Ailanthus altissima: sapling growth and recruitment in canopy gaps. J Torrey Bot Soc 127:307–315

    Article  Google Scholar 

  • Kowarik I (1995) Clonal growth in Ailanthus altissima on a natural site in West Virginia. J Veg Sci 6:853–856

    Article  Google Scholar 

  • Lavergne S, Molofsky J (2007) Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc Natl Acad Sci USA 104:3883–3888

    Article  CAS  PubMed  Google Scholar 

  • Lee CE (2002) Evolutionary genetics of invasive species. Trends Ecol Evol 17:386–391

    Article  Google Scholar 

  • Loiselle BA, Jorgensen PM, Consiglio T, Jimenez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J Biogeogr 35:105–116

    Google Scholar 

  • Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu Z, Yang L, Merchant JW (2000) Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int J Remote Sens 21:1303–1330

    Article  Google Scholar 

  • MacArthur RH (1972) Geographical ECOLOGY. Harper & Row Publishers, New York, p 269

    Google Scholar 

  • Mack RN, Simberloff D, Lonsdale WM, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689–710

    Article  Google Scholar 

  • Manly BF, MacDonald LL, McDonals TL, Thomas DL, Erickson WP (2002) Resource selection by animals: statistical design and analysis for field studies. Kluwer, Dordrecht, p 221

    Google Scholar 

  • Miller JH (2003) Nonnative invasive plants of southern forests: a field guide for identification and control. General Technical Report, p 93. USDA Forest Service, Southern Research Station, Asheville, NC

    Google Scholar 

  • Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH (2004) Spatial extrapolation: the science of predicting ecological patterns and processes. Bioscience 54:310–320

    Article  Google Scholar 

  • Mitchell CE, Agrawal AA, Bever JD, Gilbert GS, Hufbauer RA, Klironomos JN, Maron JL, Morris WF, Parker IM, Power AG, Seabloom EW, Torchin ME, Vazquez DP (2006) Biotic interactions and plant invasions. Ecol Lett 9:726–740

    Article  PubMed  Google Scholar 

  • Omernik JM (1987) Ecoregions of the conterminous United States. Ann Assoc Am Geogr 77:118–125

    Article  Google Scholar 

  • Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49:1373–1379

    Article  CAS  PubMed  Google Scholar 

  • Peterson AT (2001) Predicting species’ geographic distributions based on ecological niche modeling. Condor 103:599–605

    Article  Google Scholar 

  • Peterson AT (2003) Predicting the geography of species’ invasions via ecological niche modeling. Q Rev Biol 78:419–433

    Article  PubMed  Google Scholar 

  • R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Spiegelhalter D, Thomas A, Best N (1999) WinBUGS version 1.2 user manual. MRC Biostatistics Unit, Cambridge

    Google Scholar 

  • Trifilo P, Raimondo F, Nardini A, Lo Gullo MA, Salleo S (2004) Drought resistance of Ailanthus altissima: root hydraulics and water relations. Tree Physiol 24:107–114

    CAS  PubMed  Google Scholar 

  • USGS (1996) HYDRO1k elevation derivative database. Retrieved from http://lpdaac.usgs.gov/gtopo30/hydro/index.asp on 22 February 2004

  • Welk E (2004) Constraints in range predictions of invasive plant species due to non-equilibrium distribution patterns: Purple loosestrife (Lythrum salicaria) in North America. Ecol Modell 179:551–567

    Article  Google Scholar 

  • Williams JW, Shuman BN, Webb T (2001) Dissimilarity analyses of late-quaternary vegetation and climate in eastern North America. Ecology 82:3346–3362

    Google Scholar 

  • Yan X, Zhenyu L, Gregg WP, Dianmo L (2001) Invasive species in China—an overview. Biodivers Conserv 10:1317–1341

    Article  Google Scholar 

  • Zhang PC, Shao GF, Zhao G, Le Master DC, Parker GR, Dunning JB, Li QL (2000) Ecology—China’s forest policy for the 21st century. Science 288:2135–2136

    Article  CAS  PubMed  Google Scholar 

  • Zhu ZL, Waller E (2003) Global forest cover mapping for the United Nations Food and Agriculture Organization Forest Resources Assessment 2000 program. For Sci 49:369–380

    CAS  Google Scholar 

  • Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots—a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Research funded by United States Geological Survey grant (144-ME50). Specimen data was accessed from the following herbaria and databases: MO, ARIZ, ASU, AUA, calflora, CM, COLO, CRISIS, FTG, ILLS, INVADERS, IPANE, KUN, LL, LSU, MIL, MISS, MO, MOR, MSC, NY, OKL, OS, OSC, PH, PUL, SAT, SJSU, TAES, TAMU, TEX, UNA, USNM, WIS, JEPS, WTU, UCR, MONTU, F, NAU, UMO, SUWS, UWPL, WSP, UA, USGB, CSUC, QUE, UNB, UBC, NSPM, ALTA, SASK, PE, KUN. The assistance from these institutions is greatly appreciated. We especially thank curators and staff at Missouri Botanical Gardens, The Field Museum, and The Institute of Botany of the Chinese Academy of Sciences for exceptional assistance during our visits there. TPA thanks N. Keuler for statistical consultation, M. Turner and V. Radeloff for comments and support in completing this work, and Z. Zhu for facilitating this collaboration. QG was also supported by National Science Foundation grant (DEB-0640058) and USDA. Comments by two anonymous reviewers improved this manuscript. Author contributions: TPA, QFG, and LJC conceived the study; TPA, QFG, LJC, and CH collected the data; TPA, LJC, and CH analyzed the data; and TPA led the writing.

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Authors and Affiliations

  1. Department of Zoology, University of Wisconsin-Madison, 430 Lincoln Dr., Madison, WI, 53706, USA

    Thomas P. Albright

  2. School of Remote Sensing Information Engineering, Wuhan University, Wuhan, People’s Republic of China

    Hao Chen

  3. National Geomatics Center of China, Beijing, People’s Republic of China

    Lijun Chen

  4. Southern Research Station, US Forest Service, 200 W.T. Weaver Blvd., Asheville, NC, 28804, USA

    Qinfeng Guo

  5. Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA

    Thomas P. Albright

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  1. Thomas P. Albright
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Correspondence to Thomas P. Albright.

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Albright, T.P., Chen, H., Chen, L. et al. The ecological niche and reciprocal prediction of the disjunct distribution of an invasive species: the example of Ailanthus altissima . Biol Invasions 12, 2413–2427 (2010). https://doi.org/10.1007/s10530-009-9652-8

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