Advertisement

Biological Invasions

, Volume 13, Issue 12, pp 2785–2797 | Cite as

Use of niche models in invasive species risk assessments

  • A. Jiménez-Valverde
  • A. T. Peterson
  • J. Soberón
  • J. M. Overton
  • P. Aragón
  • J. M. LoboEmail author
Original Paper

Abstract

Risk maps summarizing landscape suitability of novel areas for invading species can be valuable tools for preventing species’ invasions or controlling their spread, but methods employed for development of such maps remain variable and unstandardized. We discuss several considerations in development of such models, including types of distributional information that should be used, the nature of explanatory variables that should be incorporated, and caveats regarding model testing and evaluation. We highlight that, in the case of invasive species, such distributional predictions should aim to derive the best hypothesis of the potential distribution of the species by using (1) all distributional information available, including information from both the native range and other invaded regions; (2) predictors linked as directly as is feasible to the physiological requirements of the species; and (3) modelling procedures that carefully avoid overfitting to the training data. Finally, model testing and evaluation should focus on well-predicted presences, and less on efficient prediction of absences; a k-fold regional cross-validation test is discussed.

Keywords

Biological invasions Model validation Occurrence data Potential distribution models 

Notes

Acknowledgments

This paper was supported by the Spanish MEC project CGL2004-0439/BOS, a Fundación BBVA Project, and the European Distributed Institute of Taxonomy (EDIT) project. AJ-V was supported by a MEC (Ministerio de Educación y Ciencia, Spain) postdoctoral fellowship (Ref.: EX-2007-0381) and the MEC Juan de la Cierva Program. ATP and JS were supported by a grant from Microsoft Research.

References

  1. Anderson RP (2003) Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. J Biogeogr 30:591–605CrossRefGoogle Scholar
  2. Anderson RP, Lew D, Peterson AT (2003) Evaluating predictive models of species’ distributions: criteria for selecting optimal models. Ecol Model 162:211–232CrossRefGoogle Scholar
  3. Angert AL (2009) The niche, limits to species’ distributions, and spatiotemporal variation in demography across the elevation ranges of two monkeyflowers. Proc Natl Acad Sci USA 106:19693–19698PubMedCrossRefGoogle Scholar
  4. Aragón P, Baselga A, Lobo JM (2010) Global estimation of invasion risk zones for the western corn rootworm Diabrotica virgifera virgifera: integrating distribution models and physiological thresholds to assess climatic favourability. J Appl Ecol 47:1026–1035CrossRefGoogle Scholar
  5. Araújo MB, Williams PH (2000) Selecting areas for species persistence using occurrence data. Biol Conserv 96:331–345CrossRefGoogle Scholar
  6. Austin M (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol Model 200:1–19CrossRefGoogle Scholar
  7. Basille M, Calenge C, Marboutin E et al (2008) Assessing habitat selection using multivariate statistics: some refinements of the ecological-niche factors analysis. Ecol Model 211:233–240CrossRefGoogle Scholar
  8. Bax N, Carlton JT, Mathews-Amos A et al (2001) The control of biological invasions in the world’s oceans. Conserv Biol 15:1234–1246CrossRefGoogle Scholar
  9. Beaumont LJ, Gallagher RV, Thuiller W et al (2009) Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions. Divers Distrib 15:409–420CrossRefGoogle Scholar
  10. Bergmans W, Blom E (2001) Invasive plants and animals. Is there a way out? The Netherlands Committee for IUCN, AmsterdamGoogle Scholar
  11. Broennimann O, Guisan A (2008) Predicting current and future biological invasions: both native and invaded ranges matter. Biol Lett 4:585–589PubMedCrossRefGoogle Scholar
  12. Broennimann O, Treier UA, Müller-Schärer H et al (2007) Evidence of climatic niche shift during biological invasion. Ecol Lett 10:701–709PubMedCrossRefGoogle Scholar
  13. Brotons L, Thuiller W, Araújo MB et al (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437–448CrossRefGoogle Scholar
  14. Bruno JF, Stachowicz JJ, Bertness MD (2003) Inclusion of facilitation into ecological theory. Trends Ecol Evol 18:119–125CrossRefGoogle Scholar
  15. Busby JR (1991) BIOCLIM: a bioclimate analysis and prediction system. In: Margules CR, Austin MP (eds) Nature conservation: cost effective biological surveys and data analysis. CSIRO, Melbourne, pp 64–68Google Scholar
  16. Calenge C, Basille M (2008) A general framework for the statistical exploration of the ecological niche. J Theor Biol 252:674–685, 543Google Scholar
  17. Calenge C, Darmon G, Basile M et al (2008) The factorial decomposition of the Mahalanobis distances in habitat selection studies. Ecology 89:555–566PubMedCrossRefGoogle Scholar
  18. Calosi P, Bilton DT, Spicer JI et al (2010) What determines a species’ geographical range? Thermal biology and latitudinal range size relationships in European diving beetles (Coleoptera, Dytiscidae). J Anim Ecol 79:194–204PubMedCrossRefGoogle Scholar
  19. Cassey P, Blackburn TM, Sol D et al (2004) Global patterns of introduction effort and establishment success in birds. Proc R Soc Lond B Biol 271:S405–S408CrossRefGoogle Scholar
  20. Chase JM, Leibold M (2003) Ecological niches: linking classical and contemporary approaches. University of Chicago Press, ChicagoGoogle Scholar
  21. Colwell RK (1992) Niche: a bifurcation in the conceptual lineage of the term. In: Keller EF, Lloyd EA (eds) Keywords in evolutionary biology. Harvard University Press, Cambridge, pp 241–248Google Scholar
  22. Colwell RK, Rangel TF (2009) Hutchinson’s duality: the once and future niche. Proc Natl Acad Sci USA 106:19651–19658PubMedCrossRefGoogle Scholar
  23. Davis MB, Shaw RG (2001) Range shifts and adaptive responses to Quaternary climate change. Science 292:673–679PubMedCrossRefGoogle Scholar
  24. Davis MA, Grime JP, Thompson K (2000) Fluctuating resources in plant communities: a general theory of invasibility. J Ecol 88:528–534CrossRefGoogle Scholar
  25. Dennis RLH, Hardy PB (1999) Targeting squares for survey: predicting species richness and incidence of species for a butterfly atlas. Global Ecol Biogeogr 8:443–454CrossRefGoogle Scholar
  26. Elith J, Burgman MA, Regan HM (2002) Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol Model 157:313–329CrossRefGoogle Scholar
  27. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  28. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–342CrossRefGoogle Scholar
  29. Engler R, Guisan A, Rechsteiner L (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J Appl Ecol 41:263–274CrossRefGoogle Scholar
  30. Farber O, Kadmon R (2003) Assessment of alternative approaches for bioclimatic modelling with special emphasis on the Mahalanobis distance. Ecol Model 160:115–130CrossRefGoogle Scholar
  31. Feinstein AR (1996) Multivariable analysis: an introduction. Yale University Press, New HavenGoogle Scholar
  32. Ficetola GF, Thuiller W, Miaud C (2007) Prediction and validation of the potential global distribution of a problematic alien invasive species, the American bullfrog. Divers Distrib 13:476–485CrossRefGoogle Scholar
  33. Fielding AH, Haworth PF (1995) Testing the generality of bird-habitat models. Conserv Biol 9:1466–1481CrossRefGoogle Scholar
  34. Fitzpatrick MC, Hargrove WW (2009) The projection of species distribution models and the problem of non-analog climate. Biodivers Conserv 18:22–2261CrossRefGoogle Scholar
  35. Fitzpatrick MC, Weltzin JF, Sanders N et al (2007) The biogeography of prediction error: why does the introduced range of the fire ant over-predict its native range? Global Ecol Biogeogr 16:24–33CrossRefGoogle Scholar
  36. Ganeshaiah KN, Barve N, Nath K et al (2003) Predicting the potential geographical distribution of the sugarcane woolly aphid using GARP and DIVA-GIS. Curr Sci India 85:1526–1528Google Scholar
  37. Gaston KJ (2003) The structure and dynamics of geographic ranges. Oxford University Press, OxfordGoogle Scholar
  38. Godsoe W (2010) I can’t define the niche but I know it when I see it: a formal link between statistical theory and the ecological niche. Oikos 119:53–60CrossRefGoogle Scholar
  39. Gomulkiewicz R, Holt RD, Barfield M (1999) The effects of density dependence and immigration on local adaptation and niche evolution in a black-hole sink environment. Theor Popul Biol 55:283–296PubMedCrossRefGoogle Scholar
  40. Gorodkov KB (1986a) Three-dimensional climatic model of potential range and some of its characteristics I. Entomol Rev 65:1–18Google Scholar
  41. Gorodkov KB (1986b) Three-dimensional climatic model of potential range and some of its characteristics II. Entomol Rev 65:19–35Google Scholar
  42. Graham CH, Ferrier S, Huettman F et al (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends Ecol Evol 19:497–503PubMedCrossRefGoogle Scholar
  43. Groves RH, Di Castri F (1996) Biogeography of mediterranean invasions. Cambridge University Press, Cambridge, p 495Google Scholar
  44. Gu W, Swihart RK (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models. Biol Conserv 116:195–203CrossRefGoogle Scholar
  45. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  46. Hanski I (1998) Metapopulation dynamics. Nature 396:41–49CrossRefGoogle Scholar
  47. 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
  48. Hirzel AH, Helfer V, Metral F (2001) Assessing habitat-suitability models with a virtual species. Ecol Model 145:111–121CrossRefGoogle Scholar
  49. Hirzel AH, Hausser J, Chessel D et al (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036CrossRefGoogle Scholar
  50. Hobbs RJ, Humphries SE (1995) An integrated approach to the ecology and management of plant invasions. Conserv Biol 9:761–770CrossRefGoogle Scholar
  51. Hortal J, Lobo JM (2005) An ED-based protocol for optimal sampling of biodiversity. Biodivers Conserv 14:2013–2947CrossRefGoogle Scholar
  52. Hortal J, Lobo JM, Jiménez-Valverde A (2007) Limitations of biodiversity databases: case study on seed-plant diversity in Tenerife, Canary Islands. Conserv Biol 21:853–863PubMedCrossRefGoogle Scholar
  53. Hortal J, Jiménez-Valverde A, Gómez J et al (2008) Historical bias in biodiversity inventories affects the observed environmental niche of the species. Oikos 117:847–858CrossRefGoogle Scholar
  54. Hutchinson GE (1978) An introduction to population ecology. Yale University Press, New HavenGoogle Scholar
  55. Jackson ST, Overpeck JT (2000) Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology 26:194–220CrossRefGoogle Scholar
  56. Jiménez-Valverde A, Lobo JM (2011) Tolerance limits, animal. In: Simberloff D, Rejmánek M (eds) Encyclopedia of biological invasions. University of California Press, CA, pp 661–663Google Scholar
  57. Jiménez-Valverde A, Ortuño V, Lobo JM (2007) Exploring the distribution of Sterocorax Ortuño, 1990 (Coleoptera, Carabidae) species in the Iberian Peninsula. J Biogeogr 34:1426–1438CrossRefGoogle Scholar
  58. Jiménez-Valverde A, Lobo JM, Hortal J (2008) Not as good as they seem: the importance of concepts in species distribution modelling. Divers Distrib 14:885–890CrossRefGoogle Scholar
  59. Jiménez-Valverde A, Nakazawa Y, Lira-Noriega A, Peterson AT (2009) Environmental correlation structure and ecological niche model projections. Biodivers Inf 6:28–35Google Scholar
  60. Jiménez-Valverde A, Lira-Noriega A, Peterson AT, Soberón J (2010) Marshalling existing biodiversity data to evaluate biodiversity status and trends in planning exercises. Ecol Res 25:947–957CrossRefGoogle Scholar
  61. Jiménez-Valverde A, Decae AE, Arnedo MA (2011) Environmental suitability of new reported localities of the funnelweb spider Macrothele calpeiana: an assessment using potential distribution modelling with presence-only techniques. J Biogeogr (in press)Google Scholar
  62. Kadmon R, Oren F, Avinoam D (2004) Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecol Appl 14:401–413CrossRefGoogle Scholar
  63. Keane RM, Crawley MJ (2002) Exotic plant invasions and the enemy release hypothesis. Trends Ecol Evol 16:199–204Google Scholar
  64. Kearney M (2006) Habitat, environment and niche: what are we modelling? Oikos 115:186–191CrossRefGoogle Scholar
  65. Kearney M, Porter WP (2004) Mapping the fundamental niche: physiology, climate and the distribution of a nocturnal lizard. Ecology 85:3119–3131CrossRefGoogle Scholar
  66. Kearney M, Porter WP, Williams C et al (2009) Integrating biophysical models and evolutionary theory to predict climatic impacts on species’ ranges: the dengue mosquito Aedes aegypti in Australia. Funct Ecol 23:528–538CrossRefGoogle Scholar
  67. Kilroy C, Snelder TH, Floerl O et al (2007) A rapid technique for assessing the suitability of areas for invasive species to New Zealand’s rivers. Divers Distrib 14:262–272CrossRefGoogle Scholar
  68. Kluza DA, Vieglais DA, Andreasen JK et al (2007) Sudden oak death: geographic risk estimates and predictions of origins. Plant Pathol 56:580–587CrossRefGoogle Scholar
  69. Kolar CS, Lodge DM (2002) Ecological predictions and risk assessment for alien fishes in North America. Science 298:1233–1236PubMedCrossRefGoogle Scholar
  70. Lambdon PW, Lloret F, Hulme PE (2008) Do alien plants on Mediterranean islands tend to invade different niches from native species? Biol Invasions 10:703–716CrossRefGoogle Scholar
  71. Leung B, Drake JM, Lodge DM (2004) Predicting invasions: propagule pressure and the gravity of Allee effects. Ecology 85:1651–1660CrossRefGoogle Scholar
  72. Lobo JM (2008a) Database records as a surrogate for sampling effort provide higher species richness estimations. Biodivers Conserv 17:873–881CrossRefGoogle Scholar
  73. Lobo JM (2008b) More complex distribution models or more representative data? Biodivers Informatics 5:14–19Google Scholar
  74. Lobo JM, Verdú JR, Numa C (2006) Environmental and geographical factors affecting the Iberian distribution of flightless Jekelius species (Coleoptera: Geotrupidae). Divers Distrib 12:179–188CrossRefGoogle Scholar
  75. Lobo JM, Baselga A, Hortal J et al (2007) How does the knowledge on the spatial distribution of species increase? Divers Distrib 13:772–780CrossRefGoogle Scholar
  76. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecol Biogeogr 17:145–151CrossRefGoogle Scholar
  77. Lobo JM, Jiménez-Valverde A, Hortal J (2010) The uncertain nature of absences and their importance in species distribution modelling. Ecography 33:103–114CrossRefGoogle Scholar
  78. Lockwood JL, Hoopes MF, Marchetti MP (2007) Invasion ecology. Blackwell, OxfordGoogle Scholar
  79. López-Darias M, Lobo JM, Gouat P (2008) Predicting potential distributions of invasive species: the exotic Barbary ground squirrel in the Canarian archipelago and the west Mediterranean region. Biol Invasions 10:1027–1040CrossRefGoogle Scholar
  80. Mack RN (2004) Global plant dispersal, naturalization, and invasion: pathways, modes and circumstances. In: Ruiz GM, Carlton JT (eds) Invasive species: vectors and management strategies. Island Press, Washington DC, pp 3–30Google Scholar
  81. MacKenzie DI, Nichols JD, Lachman GB et al (2002) Estimating site occupancy when detection probabilities are less than one. Ecology 83:2248–2255CrossRefGoogle Scholar
  82. Maron JL, Vilà M, Bommarco R et al (2004) Rapid evolution of an invasive plant. Ecol Monogr 74:261–280CrossRefGoogle Scholar
  83. Mau-Crimmins TM, Schussman HR, Geiger EL (2006) Can the invaded range of a species be predicted sufficiently using only native-range data? Lehmann lovegrass (Eragrostis lehmanniana) in the southwestern United States. Ecol Model 193:736–746CrossRefGoogle Scholar
  84. Medley KA (2010) Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Global Ecol Biogeogr 19:122–133497CrossRefGoogle Scholar
  85. Miller JR, Turner MG, Smithwick EAH et al (2004) Spatial extrapolation: the science of predicting ecological patterns and processes. Bioscience 54:310–320CrossRefGoogle Scholar
  86. Muñoz AR, Real R (2006) Assessing the potential range expansion of the exotic monk parakeet in Spain. Divers Distrib 12:656–665CrossRefGoogle Scholar
  87. Olden JD, Lawler JJ, Poff NL (2008) Machine learning methods without tears: a primer for ecologists. Q Rev Biol 83:171–193PubMedCrossRefGoogle Scholar
  88. Orr MR, Smith TB (1998) Ecology and speciation. Trends Ecol Evol 13:502–506PubMedCrossRefGoogle Scholar
  89. Parker IM, Simberloff D, Lonsdale WM et al (1999) Impact: toward a framework for understanding the ecological effects of invaders. Biol Invasions 1:3–19CrossRefGoogle Scholar
  90. Peterson AT (2003) Predicting the geography of species invasion via ecological niche modelling. Q Rev Biol 78:419–433PubMedCrossRefGoogle Scholar
  91. Peterson AT (2005a) Predicting potential geographic distributions of invading species. Curr Sci India 89:9Google Scholar
  92. Peterson AT (2005b) Kansas Gap analysis: the importance of validating distributional models before using them. Southwest Nat 50:230–236CrossRefGoogle Scholar
  93. Peterson AT (2006) Uses and requirements of ecological niche models and related distributional models. Biodivers Inf 3:59–72Google Scholar
  94. Peterson AT, Nakazawa Y (2008) Environmental data sets matter in ecological niche modelling: an example with Solenopsis invicta and Solenopsis richteri. Global Ecol Biogeogr 17:135–144Google Scholar
  95. Peterson AT, Vieglais DA (2001) Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. Bioscience 51:363–371CrossRefGoogle Scholar
  96. Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol Model 213:63–72CrossRefGoogle Scholar
  97. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  98. Pulliam HR (1988) Sources, sinks and population regulation. Am Nat 132:652–661CrossRefGoogle Scholar
  99. Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361CrossRefGoogle Scholar
  100. Reese GC, Wilson KR, Hoeting JA et al (2005) Factors affecting species distribution predictions: a simulation model experiment. Ecol Appl 15:554–564CrossRefGoogle Scholar
  101. Richardson DM, Thuiller W (2007) Home away from home objective mapping of high-risk source areas for plant introductions. Divers Distrib 13:299–312CrossRefGoogle Scholar
  102. Ricklefs RE, Schluter D (1993) Species diversity in ecological communities: historical and geographical perspectives. University of Chicago Press, ChicagoGoogle Scholar
  103. Rocchini D, Hortal J, Lengyel S, Lobo JM, Jiménez-Valverde A, Ricotta C, Bacaro G, Chiarucci A (2010) Uncertainty in species distribution mapping and the need for maps of ignorance. Progr Phys Geogr (in press)Google Scholar
  104. Rödder D, Schmidtlein S, Veith M, Lötters S (2009) Alien invasive slider turtle in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS ONE 4:e7843PubMedCrossRefGoogle Scholar
  105. Rodríguez-Trelles F, Rodríguez MA (1998) Rapid microevolution and loss of chromosomal diversity in Drosophila in response to climate warming. Evol Ecol 12:829–838CrossRefGoogle Scholar
  106. Rouget M, Richardson DM, Nel JL et al (2004) Mapping the potential ranges of major plant invaders in South Africa, Lesotho and Swaziland using climatic suitability. Divers Distrib 10:475–484CrossRefGoogle Scholar
  107. Royle JA, Nichols JD, Kéry M (2005) Modelling occurrence and abundance of species when detection is imperfect. Oikos 110:353–359CrossRefGoogle Scholar
  108. Shigesada N, Kawasaki K (1997) Biological invasions: theory and practice. Oxford University Press, OxfordGoogle Scholar
  109. Simberloff D, Parker IM, Windle PN (2005) Introduced species policy, management, and future research needs. Front Ecol Environ 3:12–20CrossRefGoogle Scholar
  110. Soberón J (2007) Grinnellian and Eltonian niches and geographic distributions of species. Ecol Lett 10:1115–1123PubMedCrossRefGoogle Scholar
  111. Soberón J, Nakamura M (2009) Niches and distributional areas: concepts, methods, and assumptions. Proc Natl Acad Sci USA 106:19644–19650PubMedCrossRefGoogle Scholar
  112. Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers Inf 2:1–10Google Scholar
  113. Steiner FM, Schlick-Steiner BC, VanDerWal J et al (2008) Combined modelling of distribution and niche in invasion biology: a case study of two invasive Tetramorium ant species. Divers Distrib 14:538–545CrossRefGoogle Scholar
  114. Stockman AK, Beamer DA, Bond JE (2006) An evaluation of a GARP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Divers Distrib 12:81–89CrossRefGoogle Scholar
  115. Stockwell DRB, Peters D (1999) The GARP modeling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Sci 13:143–158CrossRefGoogle Scholar
  116. Stockwell DRB, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148:1–13CrossRefGoogle Scholar
  117. Sutherst RW, Bourne AS (2009) Modelling non-equilibrium distributions of invasive species: a tale of two modeling paradigms. Biol Invasions 11:1231–1237CrossRefGoogle Scholar
  118. Svenning J-C, Skov F (2004) Limited filling of the potential range in European tree species. Ecol Lett 7:565–573CrossRefGoogle Scholar
  119. Thomas CD, Bodsworth EJ, Wilson RJ et al (2001) Ecological and evolutionary processes at expanding range margins. Nature 411:577–581PubMedCrossRefGoogle Scholar
  120. Thuiller W, Brotons L, Araújo MB et al (2004) Effects of restricting environmental range of data to project current and future species distributions. Ecography 27:165–172CrossRefGoogle Scholar
  121. Thuiller W, Richardson DM, Pysek P et al (2005) Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Change Biol 11:2234–2250CrossRefGoogle Scholar
  122. Václavík T, Meentemeyer RK (2009) Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions? Ecol Model 220:3248–3258CrossRefGoogle Scholar
  123. Varela S, Rodríguez J, Lobo JM (2009) Is current climatic equilibrium a guarantee for the transferability of distribution model predictions? A case study of the spotted hyena. J Biogeogr 36:1645–1655CrossRefGoogle Scholar
  124. 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 Model 179:551–567CrossRefGoogle Scholar
  125. Williamson M (1996) Biological invasions. Chapman & Hall, LondonGoogle Scholar
  126. Williamson M (1999) Invasions. Ecography 22:5–12CrossRefGoogle Scholar
  127. Zaniewski AE, Lehmann A, Overton JM (2002) Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecol Model 157:261–280CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • A. Jiménez-Valverde
    • 1
    • 2
  • A. T. Peterson
    • 1
  • J. Soberón
    • 1
  • J. M. Overton
    • 3
  • P. Aragón
    • 4
  • J. M. Lobo
    • 5
    Email author
  1. 1.Biodiversity InstituteUniversity of KansasLawrenceUSA
  2. 2.Departamento de Biología Animal, Facultad de CienciasUniversidad de MálagaMálagaSpain
  3. 3.Landcare ResearchHamiltonNew Zealand
  4. 4.Department of Ecology and Evolution (DEE), BiophoreUniversity of LausanneLausanneSwitzerland
  5. 5.Departamento de Biodiversidad y Biología EvolutivaMuseo Nacional de Ciencias Naturales (CSIC)MadridSpain

Personalised recommendations