Can the invaded range of a species be predicted sufficiently using only native-range data? Lehmann lovegrass (Eragrostis lehmanniana) in the southwestern United States

Abstract Predictions of species invasions are often made using information from their native ranges. Acquisition of native-range information can be very costly and time-consuming and in some cases may not reflect conditions in the invaded range. Using information from the invaded range can enable much faster modeling at finer geographic resolutions than using information from a species’ native range. We used confirmed presence points from the native range, southern Africa, and the invaded range, the southwestern United States, to predict the potential distribution of the perennial bunchgrass Eragrostis lehmanniana Nees, (Lehmann lovegrass), in its invaded range in the United States. The two models showed strong agreement for the area encompassed by the presence points in the invaded range, and offered insight into the overlapping but slightly different ecological niche occupied by the introduced grass in the invaded range. Regions outside of the scope of inference showed less agreement between the two models. E. lehmanniana was selected via seeding trials before being planted in the United States and therefore represents an isolated genotype from the native-range population. Models built using confirmed presence points from the invaded range can provide insight into how the selected genotype is expressed on the landscape and considers influences not present in the native range. Models created from locations in both the invaded and native ranges can lead to a more complete understanding of an introduced species’ potential for spread, especially in the case of anthropogenic selection.

[1]  David R. B. Stockwell,et al.  Induction of sets of rules from animal distribution data: a robust and informative method of data analysis , 1992 .

[2]  P. Chesson,et al.  Community ecology theory as a framework for biological invasions , 2002 .

[3]  E. Underwood,et al.  Predicting patterns of non‐native plant invasions in Yosemite National Park, California, USA , 2004 .

[4]  A. Peterson.  Predicting the Geography of Species’ Invasions via Ecological Niche Modeling , 2003, The Quarterly Review of Biology.

[5]  A. Peterson,et al.  INTERPRETATION OF MODELS OF FUNDAMENTAL ECOLOGICAL NICHES AND SPECIES' DISTRIBUTIONAL AREAS , 2005 .

[6]  A. Peterson,et al.  Preliminary distributional analysis of US endangered bird species , 2000, Biodiversity & Conservation.

[7]  S. Zalba,et al.  Using a habitat model to assess the risk of invasion by an exotic plant. , 2000 .

[8]  A. Peterson,et al.  Predicting the potential invasive distributions of four alien plant species in North America , 2003, Weed Science.

[9]  E. Welk,et al.  Constraints in range predictions of invasive plant species due to non-equilibrium distribution patterns: Purple loosestrife (Lythrum salicaria) in North America , 2004 .

[10]  J. Grinnell.  Field Tests of Theories Concerning Distributional Control , 1917, The American Naturalist.

[11]  A. Peterson,et al.  Predicting Species Invasions Using Ecological Niche Modeling: New Approaches from Bioinformatics Attack a Pressing Problem , 2001 .

[12]  A. Peterson,et al.  Using Ecological‐Niche Modeling to Predict Barred Owl Invasions with Implications for Spotted Owl Conservation , 2003 .

[13]  David R. B. Stockwell,et al.  The GARP modelling system: problems and solutions to automated spatial prediction , 1999, Int. J. Geogr. Inf. Sci..

[14]  J. Michael Scott,et al.  Predicting Species Occurrences: Issues of Accuracy and Scale , 2002 .

[15]  T. Dawson,et al.  Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? , 2003 .

[16]  A. Townsend Peterson,et al.  Predicting invasions of North American basses in Japan using native range data and a genetic algorithm , 2004 .

[17]  B. Mackey,et al.  Opportunities for Improved Risk Assessments of Exotic Species in Canada Using Bioclimatic Modeling , 2003, Environmental monitoring and assessment.

[18]  E. Welk,et al.  Present and potential distribution of invasive garlic mustard (Alliaria petiolata) in North America , 2002 .

[19]  A. Peterson,et al.  Prediction of bird community composition based on point‐occurrence data and inferential algorithms: a valuable tool in biodiversity assessments , 2002 .

[20]  Robert P. Anderson,et al.  Evaluating predictive models of species’ distributions: criteria for selecting optimal models , 2003 .

[21]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[22]  F. D. Panetta,et al.  WEED RISK ASSESSMENT , 2008 .

[23]  J. Drake,et al.  The Potential Distribution of Zebra Mussels in the United States , 2004 .

[24]  S. Higgins,et al.  Validation of a spatial simulation model of a spreading alien plant population , 2001 .

[25]  Sara J. Graves,et al.  Biological invasions: a challenge in ecological forecasting , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[26]  J. R. Cox,et al.  The influence of climate and soils on the distribution of four African grasses , 1988 .

[27]  J. Maron,et al.  A biogeographical approach to plant invasions: the importance of studying exotics in their introduced and native range , 2005 .

[28]  V. Sánchez‐Cordero,et al.  Conservatism of ecological niches in evolutionary time , 1999, Science.

[29]  A. Peterson,et al.  Effects of global climate change on geographic distributions of Mexican Cracidae , 2001 .

[30]  D. Kriticos,et al.  A comparison of systems to analyze potential weed distributions. , 2001 .

[31]  M. Hoffmann.  The distribution of Senecio vulgaris: capacity of climatic range models for predicting adventitious ranges , 2001 .

[32]  George P. Malanson,et al.  Realized versus fundamental niche functions in a model of chaparral response to climatic change , 1992 .

[33]  A. Peterson,et al.  PREDICTING SPECIES' GEOGRAPHIC DISTRIBUTIONS BASED ON ECOLOGICAL NICHE MODELING , 2001 .

[34]  George B. Ruyle,et al.  Influence of climatic and edaphic factors on the distribution of Eragrostis lehmanniana Nees in Arizona, USA. , 1986 .

[35]  A. Peterson,et al.  Lutzomyia vectors for cutaneous leishmaniasis in Southern Brazil: ecological niche models, predicted geographic distributions, and climate change effects. , 2003, International journal for parasitology.

[36]  Lori Lach,et al.  ENVIRONMENTAL AND ECONOMIC COSTS ASSOCIATED WITH NON-INDIGENOUS SPECIES IN THE UNITED STATES , 1999 .

[37]  A. Peterson,et al.  Predicting distributions of Mexican birds using ecological niche modelling methods , 2002 .

[38]  David R. B. Stockwell,et al.  Future projections for Mexican faunas under global climate change scenarios , 2002, Nature.

[39]  Elizabeth Moreno,et al.  Potential Ecological Distribution of Alien Invasive Species and Risk Assessment: a Case Study of Buffel Grass in Arid Regions of Mexico , 2004 .

[40]  R. Scachetti-Pereira,et al.  Potential Geographic Distribution of Anoplophora glabripennis (Coleoptera: Cerambycidae) in North America , 2004 .

[41]  A. Peterson,et al.  Sensitivity of distributional prediction algorithms to geographic data completeness , 1999 .

[42]  V. Sánchez‐Cordero,et al.  Museum specimen data predict crop damage by tropical rodents. , 2000, Proceedings of the National Academy of Sciences of the United States of America.