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Bentgrass Distribution Surveys and Habitat Suitability Maps Support Ecological Risk Assessment in Cultural Landscapes

Published online by Cambridge University Press:  20 January 2017

C. Ahrens ,
J. Chung ,
T. Meyer  and
C. Auer
C. Ahrens
Affiliation:
Department of Plant Science, University of Connecticut, Storrs, CT 06269
J. Chung
Affiliation:
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269
T. Meyer
Affiliation:
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269
C. Auer*
Affiliation:
Department of Plant Science, University of Connecticut, Storrs, CT 06269
*
Corresponding author's E-mail: [email protected]
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Abstract

The bentgrasses comprise an adaptable group of grasses that include introduced species, cultivated turfgrasses, and native plants in North America. Their distribution in cultural landscapes has not been documented, and this gap in knowledge has limited the development of predictive ecological risk assessments for creeping bentgrass engineered for herbicide resistance. In this study, bentgrass distribution and abundance were surveyed in 289 plots in an 8.5 km2 site surrounding a golf course in the northeastern United States. Four introduced species and two native bentgrasses were identified in seminatural and managed plant communities. Across the study site, 77% of the plots containing creeping bentgrass also had invasive plants. Bentgrasses co-occurred with critical habitat for threatened or endangered animals. Multivariate logistic regression analysis showed that bentgrasses were positively correlated with herbaceous plant cover and mowing, but negatively correlated with tree canopy cover, shrub cover, poorly drained soils, and leaf litter. The most influential ecological factors were tree canopy cover and soil moisture. Geospatial information about these two ecological factors was combined with mathematical models to generate two habitat suitability maps. The favorable environments map (FEM) showed that highly suitable bentgrass habitat covered 36% of the study site and included common features such as home lawns and railroad right-of-ways. Our results suggest that release of herbicide-resistant creeping bentgrass in this cultural landscape could potentially result in pollen-mediated gene flow, interspecific hybridization, environmental hazards, and herbicide selection pressure in some areas. Habitat suitability maps could be critical tools for predictive ecological risk assessments, monitoring projects, and management of herbicide-resistant bentgrasses.

Keywords

Creeping bentgrass, Agrostis stolonifera LAGSSTHabitat suitabilityecological risk assessmentgeospatial information
Type
Weed Biology and Ecology
Information
Weed Science , Volume 59 , Issue 2 , June 2011 , pp. 145 - 154
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Abbott, R. J., James, J. K., Milne, R. I., and Gillies, A. C. M. 2003. Plant introductions, hybridization and gene flow. Philos. T. Roy. Soc. B. 358:1123–1132.CrossRefGoogle ScholarPubMed
Akaike, H. 1973. Information theory as an extension of the maximum likelihood principle. Page 2 in Petrov, B. N. and Csaki, F., eds. Second International Symposium on Information Theory. Budapest, Hungary Akademiai Kiado.Google Scholar
Akaike, H. 1974. A new look at the statistical model identification. IEEE Trans. Automatic Control. 19:716–723.Google Scholar
Anderson, D. R. and Burnham, K. P. 2002. Avoiding pitfalls when using information-theoretic methods. J. Wildlife Manage. 66:912–918.Google Scholar
Andow, D. A. and Zwahlen, C. 2006. Assessing environmental risks of transgenic plants. Ecol. Lett. 9:196–214.Google Scholar
Auer, C. 2008. Ecological risk assessment and regulation for genetically-modified ornamental plants. Crit. Rev. Plant Sci. 27:255–271.Google Scholar
Banks, P. A., Branham, B., Harrison, K., Whitson, T., and Heap, I. 2003. Determination of the Potential Impact from the Release of Glyphosate- and Glufosinate-Resistant Agrostis stolonifera L. in Various Crop and Non-Crop Ecosystems. Weed Sci. Soc. Am. Special Rep. Lawrence, KS Weed Science Society of America.Google Scholar
Barkworth, M. E., Anderton, L. K., Capels, K. M., Long, S., and Piep, M. B., eds. 2007. Manual of Grasses for North America. Logan, UT Utah State Univ. Press. Pp. 152–154.Google Scholar
Baucom, R. S. and Mauricio, R. 2004. Fitness costs and benefits of novel herbicide tolerance in a noxious weed. P. Natl. Acad. Sci. 101:13386–13390.CrossRefGoogle Scholar
Beard, J. B. 2002. Turf management for golf courses. Second ed. Chelsea, MI Ann Arbor Press. Pp. 137–138; 716–726.Google Scholar
Behrendt, S. and Hanf, M. 1979. Grass Weeds in World Agriculture. Rhein, Germany BASF Aktiengesellschaft, Ludwigshafen am Rhein. Pp. 73–80.Google Scholar
Belanger, F. C., Meagher, T. R., Day, P. R., Plumley, K., and Meyer, W. A. 2003. Interspecific hybridization between Agrostis stolonifera and related Agrostis species under field conditions. Crop Sci. 43:240–246.Google Scholar
Bellis, L. M., Pidgeon, A. M., Radeloff, V. C., St-Louis, V., Navarro, J. L., and Martella, M. B. 2008. Modeling habitat suitability for greater rheas based on satellite image texture. Ecol. Appl. 18:1956–1966.Google Scholar
Bian, L. and West, E. 1997. GIS modeling of elk calving habitat in a prairie environment with statistics. Photogramm. Eng. Rem. S. 63:161–167.Google Scholar
Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Multimodel Interference: A Practical Information-Theoretic Approach. Second ed. New York Springer-Verlag. Pp. 49–76.Google Scholar
Cerdeira, A. L. and Duke, S. O. 2006. The current status and environmental impacts of glyphosate-resistant crops: a review. J. Environ. Qual. 35:1633–1658.Google Scholar
Chong, G. W., Otsuki, Y., Stohlgren, T. J., Guenther, D., Evangelista, P., Villa, C., and Waters, A. 2006. Evaluating plant invasions from both habitat and species perspectives. West. N. Am. Naturalist. 66:92–105.Google Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20:37–46.Google Scholar
Connecticut Invasive Plants Council. 2004. Connecticut Invasive Plant List. http://www.hort.uconn.edu/cipwg/invplantsCT05.pdf. Accessed: September 7, 2009.Google Scholar
Connecticut Invasive Plants Working Group. 2001. Invasive Plant Management Guide. http://www.hort.uconn.edu/cipwg/art_pubs/GUIDE/consideration.htm. Accessed: September 18, 2010.Google Scholar
Conner, A. J., Glare, T. R., and Nap, J. P. 2003. The release of genetically modified crops in the environment. Part II. Overview of ecological risk assessment. Plant J. 33:19–46.Google Scholar
Council for Agricultural Science and Technology. 2004. Biotechnology-Derived Perennial Turf and Forage Grasses: Criteria for Evaluation. Ames, IA Council for Agricultural Science and Technology Special Publication Number 25.Google Scholar
Craig, W., Tepfer, M., Degrassi, G., and Ripandelli, D. 2008. An overview of general features of risk assessments of genetically modified crops. Euphytica. 164:853–880.Google Scholar
Dowhan, J. J. 1979. Preliminary Checklist of the Vascular Flora of Connecticut. Harford, CT Connecticut Department of Environmental Protection. Report of Investigations No. 8, State Geological and Natural History Survey of Connecticut. 12p.Google Scholar
Ellstrand, N. C. 2003. Current knowledge of gene flow in plants: implications for transgene flow. Philos. T. Roy. Soc. B. 358:1163–1170.Google Scholar
Environmental Protection Agency. 2010. Ecological Risk Assessment. http://www.epa.gov/risk/ecological-risk.htm. Accessed: September 16, 2010.Google Scholar
Evangelista, P. H., Kumar, S., Stohlgren, T. J., Jarnevich, C. S., Crall, A. W., Norman, J. B. III, and Barnett, D. T. 2008. Modelling invasion for habitat generalist and a specialist plant species. Divers. Distrib. 14:808–817.Google Scholar
Farnsworth, E. J. and Ellis, D. R. 2001. Is purple loosestrife (Lythrum salicaria) an invasive threat to freshwater wetlands? Conflicting evidence from several ecological metrics. Wetlands. 21:199–209.Google Scholar
Fei, S. and Nelson, E. 2003. Estimation of pollen viability, shedding pattern, and longevity of creeping bentgrass on artificial media. Crop Sci. 43:2177–2181.Google Scholar
Fei, S. and Nelson, E. 2004. Greenhouse evaluation of fitness-related reproductive traits in roundup-tolerant transgenic creeping bentgrass (Agrostis Stolonifera L.). In Vitro Cell Dev. B. 40:266–273.Google Scholar
Felber, F., Kozlowski, G., Arrigo, N., and Guadagnuolo, R. 2007. Genetic and ecological consequences of transgene flow to the wild flora. Adv. Biochem. Eng. Biot. 107:173–205.Google Scholar
Fielding, A. H. and Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24:38–49.Google Scholar
Gardner, D. S., Danneberger, T. K., and Nelson, E. K. 2004. Lateral spread of glyphosate-resistant transgenic creeping bentgrass (Agrostis stolonifera) lines in established turfgrass swards. Weed Technol. 18:773–778.Google Scholar
Gardner, D. S., Nelson, E. K., Waldecker, M. A., and Tarter, W. R. 2006. Establishment and lateral growth of glyphosate-resistant creeping bentgrass in bare soil. HortTechnology. 16:590–594.Google Scholar
Glenz, C., Massolo, A., Kuonen, D., and Sclaepfer, R. 2001. A wolf habitat suitability prediction study in Valais (Switzerland). Landscape Urban Plan. 55:55–65.Google Scholar
Hart, S. E., Belanger, F. C., McCullough, P. E., and Rotter, D. 2009. Competitiveness of Agrostis interspecific hybrids in turfgrass swards. Crop Sci. 49:2275–2284.Google Scholar
Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., and Guisan, A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199:142–152.Google Scholar
Hosmer, D. W. and Lemeshow, S. 2000. Applied Logistic Regression. 2nd ed. Hoboken, NJ Wiley-Interscience Publication. Pp. 1, 92, 95.Google Scholar
Invasive Plant Atlas. 2010. Invasive Plant Atlas of the United States. http://www.invasiveplantatlas.org/grass.html. Accessed: March 12, 2010.Google Scholar
Jacobs, M. J. and Macisaac, H. J. 2009. Modelling spread of the invasive macrophyte Cabomba caroliniana . Freshwater Biol. 54:296–305.Google Scholar
Johnson, P. and Riordan, R. 1999. A review of issues pertaining to transgenic turfgrasses. HortScience. 34:594–598.CrossRefGoogle Scholar
Kirk, T. A. and Zielinski, W. J. 2009. Developing and testing landscape habitat suitability model for the American marten (Martes Americana) in the Cascades mountains of California. Landscape Ecol. 24:759–773.Google Scholar
Londo, J. P., Bautista, N. S., Sagers, C. L., Lee, E. H., and Watrud, L. S. 2010. Glyphosate drift promotes changes in fitness and transgene gene flow in canola (Brassica napus) and hybrids. Ann. Bot. DOI: 10.1093/aob/mcq190.Google Scholar
MacBryde, B. 2005. White Paper: Perspectives on creeping bentgrass, Agrostis stolonifera L. Riverdale, MD USDA/APHIS/BRS. http://www.aphis.usda.gov/peer_review/downloads/perspectiveCBG-wp.pdf. Accessed: November 3, 2009.Google Scholar
Mallory-Smith, C. and Zapiola, M. 2008. Review: gene flow from glyphosate-resistant crops. Pest Manag. Sci. 64:428–440.Google Scholar
Marvier, M., Carriere, Y., Ellstrand, N., Gepts, P., Kareiva, P., Rosi-Marshall, E., Tabashnick, B. E., and Wolfenbarger, L. L. 2008. Harvesting data from genetically engineered crops. Science. 320:452–453.Google Scholar
Metzler, K. J. and Barrett, J. P. 2006. The Vegetation of Connecticut: A Preliminary Classification. The State Geological and Natural History Survey of Connecticut. Hartford, CT Connecticut Department of Environmental Protection. Pp. 1–2.Google Scholar
Meyer, T. H., Bean, J. E., Ferguson, C. R., and Naismith, J. M. 2002. The effect of broadleaf canopies on survey-grade horizontal GPS/GLONASS measurements. Survey Land Inform. Sci. 62:215–224.Google Scholar
Nap, J. P., Metz, P. L., Escaler, M., and Conner, A. J. 2003. The release of genetically-modified crops in the environment, Part I. Overview of current status and regulations. Plant J. 33:1–18.Google Scholar
National Golf Foundation. 2010. Golf facilities in the U.S. www.ngf.org. Accessed: March 12, 2010.Google Scholar
Nielsen, C., Hartvig, P., and Kollmann, J. 2008. Predicting the distribution of the invasive alien Heracleum mantegazzianum at two different spatial scales. Divers. Distrib. 14:307–317.CrossRefGoogle Scholar
Pereira, J. M. C. and Itami, R. M. 1991. GIS-based habitat modeling using logistic multiple regression: a study of the mt. Graham red squirrel. Photogramm. Eng. Rem. S. 57:1475–1486.Google Scholar
Peterson, R. K. D., Meyer, S. J., Wolf, A. T., Wolt, J. D., and Davis, P. M. 2006. Genetically engineered plants, endangered species, and risk: a temporal and spatial exposure assessment for karner blue butterfly larvae and Bt maize pollen. Risk Anal. 26:845–858.Google Scholar
Real, R., Barbosa, A. M., and Vargas, J. M. 2006. Obtaining environmental favourability functions from logistic regression. Environ. Ecol. Stat. 13:237–245.Google Scholar
Reichman, J. R., Watrud, L. S., Lee, E. H., Burdick, C. A., Bollman, M. A., Storm, M. J., King, G. A., and Mallory-Smith, C. 2006. Establishment of transgenic herbicide-resistant creeping bentgrass (Agrostis stolonifera L.) in nonagronomic habitats. Mol. Ecol. 15:4243–4255.Google Scholar
Sanchez-Flores, E., Rodriguez-Gallegos, H., and Yool, S. R. 2008. Plant invasions in dynamic desert landscapes: a field and remote sensing assessment of predictive and change modeling. J. Arid Environ. 72:189–206.Google Scholar
Seoane, J., Carrascal, L. M., Alonso, C. L., and Palomino, D. 2005. Species-specific traits associated to prediction errors in bird habitat suitability modeling. Ecol. Model. 185:299–308.Google Scholar
Snay, R. A. 2000. The national and cooperative CORS systems in 2000 and beyond. Pages 55–58 in Proceedings of ION GPS 2000. Salt Lake City, UT National Oceanic and Atmospheric Administration.Google Scholar
Snow, A. A. 2002. Transgenic crops—why gene flow matters. Nat. Biotechnol. 20:542.Google Scholar
Snow, A. A., Andow, D. A., Gepts, P., Hallerman, E. M., Power, A., Tiedje, J. M., and Wolfenbarger, L. L. 2005. Genetically engineered organisms and the environment: current status and recommendations. Ecol. Appl. 15:377–404.Google Scholar
Stewart, C. N., Halfhill, M. D., and Warwick, S. I. 2003. Transgene introgression from genetically-modified crops to their wild relatives. Nat. Rev. Genet. 4:806–817.Google Scholar
[UNESCO] United Nations Educational, Scientific, and Cultural Organization. 2010. United Nations Educational, Scientific, and Cultural Organization. http://www.unesco.org/new/en/unesco/. Accessed: August 3, 2010.Google Scholar
[USDA NRCS] United States Department of Agriculture, Natural Resources Conservation Service. 2007. United States Department of Agriculture, Natural Resources Conservation Service. http://www.nrcs.usda.gov/technical/maps.html. Accessed: August 2009.Google Scholar
Van der Water, P. K., Watrud, L. S., Lee, E. H., Burdick, C., and King, G. A. 2007. Long-distance GM pollen movement of creeping bentgrass using modeled wind trajectory analysis. Ecol. Appl. 17:1244–1256.Google Scholar
Vergara, G. V. and Bughrara, S. S. 2003. AFLP analyses of genetic diversity in bentgrass. Crop Sci. 43:2162–2171.Google Scholar
Warwick, S. I., Legere, A., Simard, M. J., and James, T. 2008. Do escaped transgenes persist in nature? The case of an herbicide resistance transgene in a weedy Brassica rapa population. Mol. Ecol. 17:1387–1395.Google Scholar
Watrud, L. S., Lee, E. H., Fairbrother, A., Burdick, C., Reichman, J. R., Bolllman, M., Storm, M., King, G., and Van de Water, P. K. 2004. Evidence for landscape-level, pollen-mediated gene flow from genetically modified creeping bentgrass with CP4 EPSPS as a marker. P. Natl. Acad. Sci. 101:14533–14538.Google Scholar
Wiens, T. S., Dale, B. C., Boyce, M. S., and Kershaw, G. P. 2008. Three way k-fold cross validation of resource selection functions. Ecol. Model. 212:244–255.Google Scholar
Wilkinson, M. J., Elliott, L. J., Allainguillaume, J., Shaw, M. W., Norris, C., Welters, R., Alexander, M., Sweet, J., and Mason, D. C. 2003. Hybridization between Brassica napus and B. rapa on a national scale in the United Kingdom. Science. 302:457–459.Google Scholar
Wipff, J. K. and Fricker, C. R. 2000. Determining gene flow of transgenic creeping bentgrass and gene transfer to other bentgrass species. Diversity. 16:36–39.Google Scholar
Zapiola, M. L., Campbell, C. K., Butler, M. D., and Mallory-Smith, C. A. 2008. Escape and establishment of transgenic glyphosate-resistant creeping bentgrass Agrostis stolonifera in Oregon, USA: a 4-year study. J. Appl. Ecol. 45:486–494.Google Scholar