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Geospatial Assessment of Invasive Plants on Reclaimed Mines in Alabama

Published online by Cambridge University Press:  20 January 2017

Dawn Lemke ,
Callie J. Schweitzer ,
Wubishet Tadesse ,
Yong Wang  and
Jennifer A. Brown
Dawn Lemke*
Affiliation:
Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand Department of Biological and Environmental Sciences, Alabama A&M University, P.O. Box 1208, Normal, AL 35762
Callie J. Schweitzer
Affiliation:
Southern Research Station, U.S. Department of Agriculture Forest Service, P.O. Box 1568, Normal, AL 35762
Wubishet Tadesse
Affiliation:
Department of Biological and Environmental Sciences, Alabama A&M University, P.O. Box 1208, Normal, AL 35762
Yong Wang
Affiliation:
Department of Biological and Environmental Sciences, Alabama A&M University, P.O. Box 1208, Normal, AL 35762
Jennifer A. Brown
Affiliation:
Department of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand
*
Corresponding author's email:[email protected]
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Abstract

Throughout the world, the invasion of nonnative plants is an increasing threat to native biodiversity and ecosystem sustainability. Invasion is especially prevalent in areas affected by land transformation and disturbance. Surface mines are a major land transformation, and thus may promote the establishment and persistence of invasive plant communities. Using the Shale Hills region of Alabama as a case study, we assessed the use of landscape characteristics in predicting the probability of occurrence of six invasive plant species: sericea lespedeza, Japanese honeysuckle, Chinese privet, autumn-olive, royal paulownia, and sawtooth oak. Models were generated for invasive species occurrence using logistic regression and maximum entropy methods. The predicted probabilities of species occurrence were applied to the mined landscape to assess the probable prevalence of each species across the landscape. Japanese honeysuckle had the highest probable prevalence on the landscape (48% of the area), with royal paulownia having the lowest (less than 1%). Overall, 67% of the landscape was predicted to have at least one invasive plant species, with 20% of the landscape predicted to have two or more species, and 3% of the landscape predicted to have three or more species. Japanese honeysuckle, sericea lespedeza, privet, and autumn-olive showed higher occurrence on the reclaimed sites than across the broader region. We found that geospatial modeling of these invasive plants at this scale offered potential for management, both for identifying habitat types at risk and areas that need management attention. However, the most immediate action for reducing the prevalence of invasive plants on reclaimed mines is to remove invasive plants from the reclamation planting list. Three (sericea lespedeza, autumn-olive, and sawtooth oak) out of the six most common invasive plants in this study were planted as part of reclamation activities.

Keywords

Autumn-olive, Elaeagnus umbellata Thunb.Chinese privet, Ligustrum sinense Lour.Japanese honeysuckle, Lonicera japonica Thunb.royal paulownia, Paulownia tomentosa (Thunb.) Sieb. & Zucc. ex Steud.sawtooth oak, Quercus acutissima Carrutherssericea lespedeza, Lespedeza cuneata (Dumont) G. DonInvasive plantslogistic regressionmaximum entropyremote sensingspatial analysis
Type
Research
Information
Invasive Plant Science and Management , Volume 6 , Issue 3 , September 2013 , pp. 401 - 410
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Alston, K. and Richardson, D. 2006. The roles of habitat features, disturbance, and distance from putative source populations in structuring alien plant invasions at the urban/wildland interface on the Cape Peninsula. S. Afr. Biol. Conserv. 132:183–198.CrossRefGoogle Scholar
Bartuszevige, A., Gorchov, D., and Raab, L. 2006. The relative importance of landscape and community features in the invasion of an exotic shrub in a fragmented landscape. Ecography 29:213–222.CrossRefGoogle Scholar
Cain, M. D. 1991. The influence of woody and herbaceous competition on early growth of naturally regenerated loblolly and shortleaf pines. South. J. Appl. For. 15:179–185.Google Scholar
Collingham, Y. C., Wadsworth, R. A., Huntley, B., and Hulme, P. E. 2000. Predicting the spatial distribution of non-indigenous riparian weeds: issues of spatial scale and extent. J. Appl. Ecol. 37:13–27.CrossRefGoogle Scholar
Dickson, J. G., Segelquist, C. A., and Rogers, M. J. 1978. Establishment of Japanese honeysuckle in the Ozark Mountains. Pages 242–245 in Proceedings of the Southeastern Association of Fish and Wildlife Agencies.Google Scholar
Dullinger, S., Kleinbauer, I., Peterseil, J., Smolik, M., and Essl, F. 2009. Niche based distribution modelling of an invasive alien plant: effects of population status, propagule pressure and invasion history. Biol. Invasions 11:2401–2414.Google Scholar
Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J. 2011. Completion of the 2006 National Land Cover Database for the conterminous United States. Photogrammetric Eng. Remote Sens. 77:858–864.Google Scholar
Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D. 2002. The national elevation dataset. Photogrammetric Eng. Remote Sens. 68:5–11.Google Scholar
Guisan, A., Weiss, S. B., and Weiss, A. D. 1999. GLM versus CCA spatial modeling of plant species distribution. Plant Ecol. 143:107–122.Google Scholar
Gutierrez, D., Fernandez, P., Seymour, A. S., and Jordano, D. 2005. Habitat distribution models: are mutualist distributions good predictors of their associates? Ecol. Appl. 15:3–18.Google Scholar
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. 2006. Multivariate Data Analysis. 6th ed. Upper Saddle River, NJ Pearson Education. 768 p.Google Scholar
Hoffman, J. D., Aguilar-Amuchastegui, N., and Tyre, A. J. 2010. Use of simulated data from a process-based habitat model to evaluate methods for predicting species occurrence. Ecography 33:656–666.CrossRefGoogle Scholar
Hoffman, J. D., Narumalani, S., Mishra, D. R., Merani, P., and Wilson, R. G. 2008. Predicting potential occurrence and spread of invasive plant species along the North Platte River, Nebraska. Invasive Plant Sci. 1:359–367.Google Scholar
Holl, K. D. 2002. Long-term vegetation recovery on reclaimed coal surface mines in the eastern USA. J. Appl. Ecol. 39:960–970.Google Scholar
Holl, K. D. and Cairns, J. J. 2002. Monitoring ecological restoration. Pages 411–432 in Perrow, M. R., and Davy, A. J., eds. Handbook of Ecological Restoration. Cambridge, UK Cambridge University Press.Google Scholar
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M. 2004. Development of a 2001 national land-cover database for the United States. Photogrammetric Eng. Remote Sens.70 829–840.Google Scholar
Hosmer, D. W. and Lemeshow, S. 2000. Applied Logistic Regression. New York Wiley-Interscience. 373 p.Google Scholar
Huntley, C. R. and Hopkins, J. C. 1979. Establishment of sawtooth oak as a mast source for wildlife. Wildl. Soc. Bull. 7:253–258.Google Scholar
Kumar, S., Stohlgren, T. J., and Chong, G. W. 2006. Spatial heterogeneity influences native and alien plant species richness. Ecology 87:3186–3199.Google Scholar
Lemke, D., Hulme, P. E., Brown, J. A., and Tadesse, W. 2011. Distribution modeling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA. For. Ecol. Manag. 262:139–149.Google Scholar
Lemke, D., Schweitzer, C. J., Tazisong, I. A., Wang, Y., and Brown, J. A. 2012. Invasion of a mined landscape: what habitat characteristics are influencing the occurrence of invasive plants? Int. J. Min. Reclam. Environ. DOI:10.1080/17480930.2012.699215Google Scholar
Lockwood, J. L., Hoopes, M. F., and Marchetti, M. P. 2007. Invasion Ecology. Malden, MA Blackwell. 304 p.Google Scholar
Manel, S., Williams, H. C., and Ormerod, S. J. 2002. Evaluating presence–absence models in ecology: the need to account for prevalence. J. Appl. Ecol. 38:921–931.Google Scholar
McGrath, D. A., Evans, J. P., Smith, C. K., Haskell, D. G., Pelkey, N. W., Gottfried, R. R., Brockett, C. D., Lane, M. D., and Williams, E. D. 2004. Mapping land use change and monitoring the impacts of hardwood-to-pine conversion on the Southern Cumberland Plateau in Tennessee. Earth Interact. 8:1–24.Google Scholar
Merriam, R. W. and Feil, E. 2002. The potential impact of an introduced shrub on native plant diversity and forest regeneration. Biol. Invasions 4:369–373.Google Scholar
Miller, J. H., Chambliss, E. B., and Loewenstein, N. J. 2010. A Field Guide for the Identification of Invasive Plants in Southern Forests. Asheville, NC U.S. Department of Agriculture, Forest Service, Southern Research Station Gen. Tech. Rep. SRS–119. 126 p.Google Scholar
Miller, J. H., Lemke, D., and Coulston, J. 2013. The Southern Forest Futures Project. Chapter 15 in The Invasion of Southern Forests by Nonnative Plants: Current and Future Occupation with Impacts, Management Strategies, and Mitigation Approaches. Gen. Tech. Rep. Asheville, NC USDA-Forest Service, Southern Research Station. In Press.Google Scholar
Oommen, T., Baise, L. G., and Vogel, R. M. 2010. Sampling bias and class imbalance in maximum-likelihood logistic regression. Math. Geosci. 43:99–120.CrossRefGoogle Scholar
Patterson, D. 1976. The history and distribution of five exotic weeds in North Carolina. Castanea 41:177–180.Google Scholar
Phillips, S., Anderson, R., and Schapire, R. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190:231–259.Google Scholar
Pickett, S.T.A. and Cadenasso, M. L. 1995. Landscape ecology: spatial heterogeneity in ecological systems. Science 269:331–334.CrossRefGoogle ScholarPubMed
Ricciardi, A. 2007. Are modern biological invasions an unprecedented form of global change? Conserv. Biol. 21:329–336.CrossRefGoogle ScholarPubMed
Robertson, M. P., Villet, M. H., and Palmer, A. R. 2004. A fuzzy classification technique for predicting species' distributions: applications using invasive alien plants and indigenous insects. Divers. Distrib. 10:461–474.CrossRefGoogle Scholar
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C. 1974. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. Greenbelt, MD NASA/GSFCT Type III Final Report. 371 p.Google Scholar
Sather, N. and Eckardt, N. 1987. Element Stewardship Abstract for Elaeagnus umbellata (Autumn Olive). Arlington, VA The Nature Conservancy. 4 p.Google Scholar
Skelly, and Loy Engineers, . 1979. A Compliance Manual—Methods for Meeting OSM Requirements. Pennsylvania State University, McGraw Hill. 600 p.Google Scholar
Smalley, G. W. 1979. Classification and evaluation of forest sites on the Southern Cumberland Plateau. New Orleans, LA U.S. Department of Agriculture, Forest Service. Southern Forest Experiment Station Gen. Tech. Rep. SO-2 3. 59 p.Google Scholar
Stevens, D. L. and Olsen, A. R. 2004. Spatially balanced sampling of natural resources. J. Am. Stat. Assoc. 99:262–278.Google Scholar
Stohlgren, T. J., Binkley, D., Chong, G. W., Kalkhan, M. A., Schell, L. D., Bull, K. A., Otsuki, Y., Newman, G., Bashkin, M., and Son, Y. 1999. Exotic plant species invade hot spots of native plant diversity. Ecol. Monogr. 69:25–46.Google Scholar
Stohlgren, T. J., Chong, G. W., Schell, L. D., Rimar, K. A., Otsuki, Y., Lee, M., Kalkhan, M. A., and Villa, C. A. 2002. Assessing vulnerability to invasion by nonnative plant species at multiple spatial scales. Environ. Manag. 29:566–577.CrossRefGoogle ScholarPubMed
Underwood, E. C., Klinger, R., and Moore, P. E. 2004. Predicting patterns of alien plant invasions in Yosemite National Park, California, USA. Divers. Distrib. 10:447–459.CrossRefGoogle Scholar
[USDA] U.S. Department of Agriculture. 2011. PLANTS National Plants Database. http://plants.usda.gov. Accessed October 2011.Google Scholar
Vitousek, P. M., D'Antonio, C. M., Loope, L. L., Rejmanek, M., and Westbrooks, R. 1997. Introduced species: a significant component of human-caused global change. N. Z. J. Ecol. 21:1–16.Google Scholar
Vogelmann, J. E., Howard, S. M., Yang, L., Larson, C. R., Wylie, B. K., and Van Driel, J. N. 2001. Completion of the 1990's National Land Cover Data Set for the conterminous United States. Photogrammetric Eng. Remote Sens. 67:650–662.Google Scholar
Wagner, H. H. and Fortin, M. J. 2005. Spatial analysis of landscapes: concepts and statistics. Ecology 86:1975–1987.Google Scholar
Wear, D. N. and Greis, J. G. 2002. Southern forest resource assessment: summary of findings. J. For. 100:6–14.Google Scholar
With, K. A. and Crist, T. O. 1995. Critical thresholds in species' responses to landscape structure. Ecology 76:2446–2459.Google Scholar
Yates, E. D., Levia, D. F., and Williams, C. L. 2004. Recruitment of three non-native invasive plants into a fragmented forest in southern Illinois. For. Ecol. Manag. 190:119–130.Google Scholar
Zeleznik, J. D. and Skousen, J. G. 1996. Survival of three tree species on old reclaimed surface mines in Ohio. J. Environ. Qual. 25:1429–1435.Google Scholar
Zipper, C. E., Burger, J. A., McGrath, J., Rodrigue, J. A., and Holtzman, G. I. 2011. Forest restoration potential of coal-mined lands in the eastern United States. J. Environ. Qual. 40:1567–1577.Google Scholar