Biological Invasions

, Volume 12, Issue 8, pp 2413–2427 | Cite as

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

  • Thomas P. AlbrightEmail author
  • Hao Chen
  • Lijun Chen
  • Qinfeng Guo
Original Paper


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.


Distribution models Ecological niche Generalized linear models Invasive species Simple Bayes Tree-of-heaven 



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.

Supplementary material

10530_2009_9652_MOESM1_ESM.pdf (115 kb)
Supplementary material 1 (PDF 115 kb)
10530_2009_9652_MOESM2_ESM.pdf (16 kb)
Supplementary material 2 (PDF 15 kb)


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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Thomas P. Albright
    • 1
    • 5
    Email author
  • Hao Chen
    • 2
  • Lijun Chen
    • 3
  • Qinfeng Guo
    • 4
  1. 1.Department of ZoologyUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.School of Remote Sensing Information EngineeringWuhan UniversityWuhanPeople’s Republic of China
  3. 3.National Geomatics Center of ChinaBeijingPeople’s Republic of China
  4. 4.Southern Research Station, US Forest ServiceAshevilleUSA
  5. 5.Department of Forest and Wildlife EcologyUniversity of Wisconsin-MadisonMadisonUSA

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