Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-14T23:28:57.513Z Has data issue: false hasContentIssue false

Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)

Published online by Cambridge University Press:  20 January 2017

Gary N. Ervin*
Affiliation:
Department of Biological Sciences, Mississippi State University, MS 39762
D. Christopher Holly
Affiliation:
Department of Biological Sciences, Mississippi State University, MS 39762
*
Corresponding author's E-mail: gervin@biology.msstate.edu

Abstract

Species distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Current address: 1154 East Wellsgate Drive, Oxford, MS, 38655

References

Literature Cited

Akobundu, I. O. and Ekeleme, F. E. 2000. Effect of method of Imperata cylindrica management on maize grain yield in the derived savanna of south-western Nigeria. Weed Res. 40:335341.Google Scholar
Anderson, R. P., Lew, D., and Peterson, A. T. 2003. Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecol. Model. 162:211232.Google Scholar
Biodiversity and Spatial Information Center. 2010a. Southeastern GAP Land Cover Dataset. Biodiversity and Spatial Information Center, USGS North Carolina Cooperative Fish and Wildlife Research Unit, North Carolina State University. http://www.basic.ncsu.edu/segap. Accessed June 24, 2010.Google Scholar
Biodiversity and Spatial Information Center. 2010b. Southeastern GAP Analysis Project—Land Cover Legend. Biodiversity and Spatial Information Center, USGS North Carolina Cooperative Fish and Wildlife Research Unit, North Carolina State University. http://www.basic.ncsu.edu/segap. Accessed April 11, 2011.Google Scholar
Bryson, C. T. and Carter, R. 1993. Cogongrass, Imperata cylindrica, in the United States. Weed Technol. 7:10051009.Google Scholar
Bryson, C. T., Krutz, L. J., Ervin, G. N., Reddy, K. N., and Byrd, J. D. Jr. 2010. Ecotype variability and edaphic characteristics for cogongrass (Imperata cylindrica) populations in Mississippi. Invasive Plant Sci. Manag. 3:199207.Google Scholar
Buchan, L. A. J. and Padilla, D. K. 2000. Predicting the likelihood of Eurasian watermilfoil presence in lakes, a macrophyte monitoring tool. Ecol. Appl. 10:14421455.Google Scholar
De Meyer, M., Robertson, M. P., Mansell, M. W., Ekesi, S., Tsuruta, K., Mwaiko, W., Vayssières, J-F., and Peterson, A. T. 2010. Ecological niche and potential geographic distribution of the invasive fruit fly Bactrocera invadens (Diptera, Tephritidae). Bull. Entomol. Res. 100:3548.Google Scholar
DiTomaso, J. M. 2000. Invasive weeds in rangelands: species, impacts, and management. Weed Sci. 48:255265.Google Scholar
Eiswerth, M. E. and Johnson, W. S. 2002. Managing nonindigenous invasive species: Insights from dynamic analysis. Environ. Resour. Econ. 23:319342.Google Scholar
Elith, J., Graham, C. H., Anderson, R. P., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129151.Google Scholar
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., and Yates, C. J. 2011. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17:4357.Google Scholar
Engler, R., Guisan, A., and Rechsteiner, L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. J. Appl. Ecol. 41:263274.Google Scholar
Faircloth, W. 2007. Managing cogongrass on rights-of-way: a challenge to prevent future spread. Pages 3437 in Loewenstein, N. J. and Miller, J. H., eds. Proceedings of the Regional Cogongrass Conference: A Cogongrass Management Guide. Mobile, AL Alabama Cooperative Extension System, Auburn University.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. 24:3849.Google Scholar
Graf, R. F., Bollmann, K., Sachot, S., Suter, W., and Bugmann, H. 2006. On the generality of habitat distribution models: a case study of Capercaillie in three Swiss regions. Ecography 29:318328.Google Scholar
Holly, D. C. 2008. Multi-scale evaluation of mechanisms associated with the establishment of a model invasive species in Mississippi: Imperata cylindrica . Ph.D dissertation, Mississippi State, MS: Mississippi State University.Google Scholar
Holly, D. C. and Ervin, G. N. 2007. Effects of intraspecific seedling density, soil type, and light availability upon growth and biomass allocation in cogongrass (Imperata cylindrica). Weed Technol. 21:812819.Google Scholar
Homer, C., Huang, C., Yang, L., Wylie, B., and Coan, M. 2004. Development of a 2001 national landcover database for the United States. Photogramm. Eng. Remote Sens. 70:829840 http://www.mrlc.gov/pdf/July_PERS.pdf. Accessed August 4, 2006.Google Scholar
Huang, C., Yang, L., Wylie, B., and Homer, C. 2001. A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas. Unpaginated CD-ROM in Third International Conference on Geospatial Information in Agriculture and Forestry. Washington, DC U.S. Department of Agriculture.Google Scholar
Kozak, K. H., Graham, C. H., and Wiens, J. J. 2008. Integrating GIS-based environmental data into evolutionary biology. Trends Ecol. Evol. 23:141148.Google Scholar
Lippincott, C. L. 2000. Effects of I. cylindrica (cogongrass) invasions on fire regimes in Florida sandhill. Nat. Area J. 20:140149.Google Scholar
Liu, C., Berry, P. M., Dawson, T. P., and Pearson, R. G. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385393.Google Scholar
Lobo, J. M., Jiménez-Valverde, A., and Real, R. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecol. Biogeogr 17:145151.Google Scholar
Loewenstein, N. J. and Miller, J. H., eds. 2007. Proceedings of the Regional Cogongrass Conference: A Cogongrass Management Guide. Mobile, AL Alabama Cooperative Extension System, Auburn University, AL. 77 p.Google Scholar
MacDonald, G. E. 2004. Cogongrass (Imperata cylindrica) biology, ecology, and management. Crit. Rev. Plant Sci. 23:367380.Google Scholar
MacDonald, G. E. 2007. Cogongrass: The plant's biology, distribution, and impacts in the southeastern U.S. Pages 1023 in Loewenstein, N. J. and Miller, J. H., eds. Proceedings of the Regional Cogongrass Conference: A Cogongrass Management Guide. Mobile, AL Alabama Cooperative Extension System, Auburn University, AL.Google Scholar
Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S., and Venette, R. C. 2007. Optimal detection and control strategies for invasive species management. Ecol. Econ. 61:237245.Google Scholar
Mullin, B. H., Anderson, L. W. J., DiTomaso, J. M., Eplee, R. E., and Getsinger, K. D. 2000. Invasive plant species. Ames, IA Council for Agricultural Science and Technology Issue Paper 13.Google Scholar
Pearson, R. G., Raxworthy, C. J., Nakamura, M., and Peterson, A. T. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34:102117.Google Scholar
Pearson, R. G., Thuiller, W., Araújo, M. B., Martinez-Meyer, E., Brotons, L., McClean, C., Miles, L., Segurado, P., Dawson, T. P., and Lees, D. C. 2006. Model-based uncertainty in species range prediction. J. Biogeogr. 33:17041711.Google Scholar
Peterson, A. T., Papes, M., and Eaton, M. 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and MaxEnt. Ecography 30:550560.Google Scholar
Peterson, A. T., Papes, M., and Kluza, D. A. 2003. Predicting the potential invasive distributions of four alien plant species in North America. Weed Sci. 51:863868.Google Scholar
Peterson, A. T., Papes, M., and Soberón, J. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecol. Model. 213:6372.Google Scholar
Phillips, S. J. 2005. A Brief Tutorial on MaxEnt. http://www.cs.princeton.edu/∼schapire/maxent/ Accessed June 14, 2010.Google Scholar
Phillips, S. J. 2008. Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography 31:272278.Google Scholar
Phillips, S. J. and Dudík, M. 2008. Modeling of species distributions with MaxEnt: New extensions and a comprehensive evaluation. Ecography 31:161175.Google Scholar
Phillips, S. J., Anderson, R. P., and Schapire, R. E. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190:231259 http://www.cs.princeton.edu/∼schapire/maxent Accessed June 14, 2010.Google Scholar
Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M. and Guisan, A. 2006. Are niche-based species distribution models transferable in space? J. Biogeogr. 33:16891703.Google Scholar
Rejmánek, M. and Pitcairn, M. J. 2004. When is eradication of exotic pests a realistic goal? Pages 249253 in Veitch, C. R. and Clout, M. N., eds. Turning the tide: the eradication of invasive species. Gland, Switzerland and Cambridge, UK SSC Invasive Species Specialist Group, International Union for the Conservation of Nature.Google Scholar
Soil Survey Staff, Natural Resources Conservation Service, U.S. Department of Agriculture. 2005. Soil Survey Geographic (SSURGO) Database for Mississippi. http://soildatamart.nrcs.usda.gov. Accessed July 21, 2010.Google Scholar
Soil Survey Staff, Natural Resources Conservation Service, U.S. Department of Agriculture. 2008. Soil Data Viewer 5.2. http://soils.usda.gov/sdv. Accessed May 17, 2010.Google Scholar
Soil Survey Staff, Natural Resources Conservation Service, U.S. Department of Agriculture. 2011. SSURGO Soil Map Coverage versus the U.S. General Soil Map Coverage. http://soildatamart.nrcs.usda.gov/USDGSM.aspx. Accessed April 10, 2011.Google Scholar
Tabor, P. 1949. Cogongrass, Imperata cylindrica (L.) Beauv., in the southeastern United States. Agron. J. 41:270.Google Scholar
Tabor, P. 1952. Cogongrass in Mobile County, Alabama. Agron. J. 44:50.Google Scholar
Terry, P. J., Adjiers, G., Akobundu, I. O., Anoka, A. U., Drilling, M. E., Tjitrosemito, S., and Utomo, M. 1997. Herbicides and mechanical control of Imperata cylindrica as a first step in grassland rehabilitation. Agrofor. Syst. 36:151179.Google Scholar
Wadsworth, R. A., Collingham, Y. C., Willis, S. G., Huntley, B., and Hulme, P. E. 2000. Simulating the spread and management of alien riparian weeds: are they out of control? J. Appl. Ecol. 37:2838.Google Scholar
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. 179:551567.Google Scholar
Westbrooks, R. G. 2004. New approaches for early detection and rapid response to invasive plants in the United States. Weed Technol. 18:14681471.Google Scholar
Yager, L. and Smith, M. 2009. Use of GIS to prioritize cogongrass (Imperata cylindrica) control on Camp Shelby Joint Forces Training Center, Mississippi. Invasive Plant Sci. Manag. 2:7482.Google Scholar
Yager, L., Jones, J., and Miller, D. L. 2009. Military training and road effects on Imperata cylindrica (L.) Beauv. (Cogongrass). Southeast. Nat. 8:695708.Google Scholar
Zimmermann, N. E., Edwards, T. C., Moisen, G. G., Frescino, T. S., and Blackard, J. A. 2007. Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah. J. Appl. Ecol. 44:10571067.Google Scholar