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Comparison of Transect-Based Standard and Adaptive Sampling Methods for Invasive Plant Species

Published online by Cambridge University Press:  20 January 2017

Bruce D. Maxwell ,
Vickie Backus ,
Matthew G. Hohmann ,
Kathryn M. Irvine ,
Patrick Lawrence ,
Erik A. Lehnhoff  and
Lisa J Rew
Bruce D. Maxwell*
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Vickie Backus
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Matthew G. Hohmann
Affiliation:
Engineer Research and Development Center, Construction Engineering Research Laboratory, Champaign, IL 61826
Kathryn M. Irvine
Affiliation:
Department of Mathematical Sciences, Montana State University, Bozeman, MT 59717
Patrick Lawrence
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Erik A. Lehnhoff
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Lisa J Rew
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
*
Corresponding author's E-mail: bmax@montana.edu
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Abstract

Early detection of an invading nonindigenous plant species (NIS) may be critical for efficient and effective management. Adaptive survey sampling methods may provide unbiased sampling for best estimates of distribution of rare and spatially clustered populations of plants in the early stages of invasion. However, there are few examples of these methods being used for nonnative plant surveys in which travelling distances away from an initial or source patch, or away from a road or trail, can be time consuming due to the topography and vegetation. Nor is there guidance as to which of the many adaptive methods would be most appropriate as a basis for invasive plant mapping and subsequent management. Here we used an empirical complete census of four invader species in early to middle stages of invasion in a management area to assess the effectiveness and efficiency of three nonadaptive methods, four adaptive cluster methods, and four adaptive web sampling methods that all originated from transects. The adaptive methods generally sampled more NIS-occupied cells and patches than standard transect approaches. Sampling along roads only was time-efficient and effective, but only for species with restricted distribution along the roads. When populations were more patchy and dispersed over the landscape the adaptive cluster starting at the road generally proved to be the most time-efficient and effective NIS detection method.

Keywords

Canada thistleCirsium arvense (L.) Scop.Dalmatian toadflaxLinaria dalmatica (L.) P. Mill.smooth bromeBromus inermis Leyss.common St. JohnswortHypericum perforatum L.early detection rapid responseEDRRnonnative plantssurveyweed mappingexotic speciesinventorycensus
Type
Research Article
Information
Invasive Plant Science and Management , Volume 5 , Issue 2 , June 2012 , pp. 178 - 193
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Acharya, B., Bhattarai, G., de Gier, A., and Stein, A. 2000. Systematic adaptive cluster sampling for the assessment of rare tree species in Nepal. Forest Ecol. Manag. 137:65–73.Google Scholar
Bock, J. H. and Bock, C. E. 2006. A survey of the vascular plants and birds of Little Bighorn National Battlefield. Final Report on CESU task agreement CA-1200-99-007. http://www.friendslittlebighorn.com/LIBIFinalReportplantsbirds.pdf.Google Scholar
Brown, J. A. 2003. Designing an efficient adaptive cluster sample. Environ. Ecol. Stat. 10:95–105.Google Scholar
Christman, M. C. 2000. A review of quadrat-based sampling of rare, geographically clustered populations. J. Agric. Biol. Environ. Stat. 5:201–234.Google Scholar
Cousens, R. and Mortimer, G. 1995. Dynamics of Weed Populations. Cambridge, UK Cambridge University Press. 332 p.Google Scholar
Gattone, S. A. and Di Battista, T. 2011. Adaptive cluster sampling with a data driven stopping rule. Stat. Methods Appl. 20:1–21.Google Scholar
Gelbard, J. L. and Belnap, J. 2003. Roads as conduits for exotic plant invasions in a semiarid landscape. Conserv. Biol. 17:420–432.Google Scholar
Huebner, C. 2007. Detection and monitoring of invasive exotic plants: a comparison of four sampling methods. Northeast. Nat. 14:183–206.Google Scholar
Lehnhoff, E. and Lawrence, P. 2010. Revised Inventory of Non-Indigenous Plants at Little Bighorn Battlefield National Monument. Final Report on CESU task agreement J1380099006. http://www.cfc.umt.edu/CESU/NEWCESU/Assets/Individual%20Project%20Reports/NPS%20Projects/MSU/2009/09Lehnhoff_LIBI_weed%20inventory_Fnl_rpt.pdf.Google Scholar
Lehnhoff, E. A., Rew, L. J., Maxwell, B. D., and Taper, M. L. 2008. Quantifying invasiveness: a case study of Linaria vulgaris . Invasive Plant Sci. Manag. 1:319–325.Google Scholar
Maxwell, B. D., Lehnhoff, E. A., and Rew, L. J. 2009. The rationale for monitoring invasive plant populations as a crucial step for management. Invasive Plant Sci. Manag. 2:1–9.Google Scholar
Moody, M. E. and Mack, R. N. 1988. Controlling the spread of plant invasions: the importance of nascent foci. J. Appl. Ecol. 25:1009–1021.Google Scholar
Morrison, L. W., Smith, D. R., Young, C. C., and Nichols, D. W. 2008. Evaluating sampling designs by computer simulation: a case study with the Missouri bladderpod. Popul. Ecol. 50:417–425.Google Scholar
Parendes, L. A. and Jones, J. A. 2000. Role of light availability and dispersal in exotic plant invasion along roads and streams in the H. J. Andrews Experimental Forest, Oregon. Conserv. Biol. 14:64–75.Google Scholar
Pauchard, A. and Alaback, P. B. 2004. Influence of elevation, land use, and landscape context on patterns of alien plant invasions along roadsides in protected areas of south-central Chile. Conserv. Biol. 18:238–251.Google Scholar
Philippi, T. 2005. Adaptive cluster sampling for estimation of abundances within local populations of low-abundance plants. Ecology 86:1091–1100.Google Scholar
Prather, T. S. 2006. Adaptive sampling design. Inventory and survey methods for nonindigenous plant species. Pages 56–59 in Rew, L. J. and Pokorny, M. L., eds. Inventory and Survey Methods for Nonindigenous Plant Species. Bozeman, MT Montana State University Extension.Google Scholar
Rew, L. J., Lehnhoff, E. A., and Maxwell, B. D. 2007. Non-indigenous species management using a population prioritization framework. Can. J. Plant Sci. 87:1029–1036.Google Scholar
Rew, L. J., Maxwell, B. D., and Aspinall, R. 2005. Predicting the occurrence of nonindigenous species using environmental and remotely sensed data. Weed Sci. 53:236–241.Google Scholar
Rew, L. J., Maxwell, B. D., Dougher, F. L., and Aspinall, R. 2006. Searching for a needle in a haystack: evaluating survey methods for non-indigenous plant species. Biol. Invasions 8:523–539.Google Scholar
Salehi, M. M. and Seber, G. A. F. 1997. Two stage adaptive cluster sampling. Biometrics 53:959–970.Google Scholar
Salehi, M. M. and Smith, D. R. 2005. Two stage sequential sampling: a neighborhood free adaptive sampling procedure. J. Agr. Biol. Environ. Stud. 10:84–102.Google Scholar
Sharma, G. P. and Raghubanshi, A. S. 2009. Plant invasions along roads: a case study from central highlands, India. Environ. Monit. Assess. 157:191–198.Google Scholar
Smith, D. R., Brown, J. A., and Lo, N. 2004. Applications of adaptive sampling to biological populations. Pages 77–122 in Thompson, W. L., ed. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters. Washington, D.C. Island Press.Google Scholar
Spellerberg, I. F. 1998. Ecological effects of roads and traffic: a literature review. Global Ecol. Biogeogr. 7:317–333.Google Scholar
Su, Z. and Quinn, T. J. II. 2003. Estimator bias and efficiency for adaptive cluster sampling with order statistics and a stopping rule. Environ. Ecol. Stat. 10:17–41.Google Scholar
Thompson, S. K. 1990. Adaptive cluster sampling. J. Am. Stat. Assoc. 85:1050–1059.Google Scholar
Thompson, S. K. 1991a. Adaptive cluster sampling: designs with primary and secondary units. Biometrics 47:1103–1115.Google Scholar
Thompson, S. K. 1991b. Stratified adaptive cluster sampling. Biometrika 78:389–397.Google Scholar
Thompson, S. K. 2002. Sampling. 2nd ed. New York Wiley. 400 p.Google Scholar
Thompson, S. K. 2006. Adaptive web sampling. Biometrics 62:1224–1234.Google Scholar
Thompson, S. K. and Seber, G. A. F. 1996. Adaptive Sampling. New York Wiley. 288 p.Google Scholar
Thompson, W. L. 2004. Future directions in estimating abundance of rare or elusive species. Pages 389–399 in Thompson, W. L., ed. Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters. Washington, DC Island.Google Scholar
Vincent, K. S. 2008. Design Variations in Adaptive Web Sampling. M.Sc. Thesis. Burnaby, BC, Canada: Simon Fraser University. 54 p.Google Scholar
Watkins, R. Z., Chen, J., Pickens, J., and Brosofske, K. D. 2003. Effects of forest roads on understory plants in a managed hardwood landscape. Conserv. Biol. 17:411–419.Google Scholar