Annals of Operations Research

, Volume 271, Issue 2, pp 357–403 | Cite as

A review of operations research models in invasive species management: state of the art, challenges, and future directions

  • İ. Esra BüyüktahtakınEmail author
  • Robert G. Haight
Original - Survey or Exposition


Invasive species are a major threat to the economy, the environment, health, and thus human well-being. The international community, including the United Nations’ Global Invasive Species Program (GISP), National Invasive Species Council (NISC), and Center for Invasive Species Management (CISM), has called for a rapid control of invaders in order to minimize their adverse impacts. The effective management of invasive species is a highly complex problem requiring the development of decision tools that help managers prioritize actions most efficiently by considering corresponding bio-economic costs, impacts on ecosystems, and benefits of control. Operations research methods, such as mathematical programming models, are powerful tools for evaluating different management strategies and providing optimal decisions for allocating limited resources to control invaders. In this paper, we summarize the mathematical models applied to optimize invasive species prevention, surveillance, and control. We first define key concepts in invasive species management (ISM) in a framework that characterizes biological invasions, associated economic and environmental costs, and their management. We then present a spatio-temporal optimization model that illustrates various biological and economic aspects of an ISM problem. Next, we classify the relevant literature with respect to modeling methods: optimal control, stochastic dynamic programming, linear programming, mixed-integer programming, simulation models, and others. We further classify the ISM models with respect to the solution method used, their focus and objectives, and the specific application considered. We discuss limitations of the existing research and provide several directions for further research in optimizing ISM planning. Our review highlights the fact that operations research could play a key role in ISM and environmental decision-making, in particular closing the gap between the decision-support needs of managers and the decision-making tools currently available to management.


Invasive species management Biological invasions Ecology Operations research Mathematical models Optimization Solution methods Decision-support tools Review 



We gratefully acknowledge the support of the US Department of Agriculture, Forest Service, Northern Research Station Joint Venture Agreement No. 16-JV-11242309-109 and the National Science Foundation CAREER Award under Grant No. CBET-1554018. We thank Stephanie Snyder and Denys Yemshanov for their invaluable suggestions and insights, which have improved the presentation and clarity of this manuscript. The authors are also grateful for the comments of the editor and an anonymous referee, whose remarks helped to improve the exposition of this paper.


  1. Aadland, D., Sims, C., & Finnoff, D. (2015). Spatial dynamics of optimal management in bioeconomic systems. Computational Economics, 45, 1–33.Google Scholar
  2. Ackoff, R. L. (1961). Progress in operations research (Vol. I). New York: Wiley.Google Scholar
  3. Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows: Theory, algorithms, and applications. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  4. Albers, H. J., Fischer, C., & Sanchirico, J. N. (2010). Invasive species management in a spatially heterogeneous world: Effects of uniform policies. Resource and Energy Economics, 32(4), 483–499.Google Scholar
  5. Altay, N., & Green, W. G, I. I. I. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475–493.Google Scholar
  6. Aukema, J. E., Leung, B., Kovacs, K., Chivers, C., Britton, K. O., Englin, J., et al. (2011). Economic impacts of non-native forest insects in the continental United States. PloS ONE, 6(9), e24587.Google Scholar
  7. Austin, Z., Cinderby, S., Smart, J. C. R., Raffaelli, D., & White, P. (2009). Mapping wildlife: Integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS. Wildlife Research, 36(7), 553–564.Google Scholar
  8. Baker, C. M., & Bode, M. (2013). Spatial control of invasive species in conservation landscapes. Computational Management Science, 10(4), 331–351.Google Scholar
  9. Batabyal, A. A., & Beladi, H. (2006). International trade and biological invasions: A queuing theoretic analysis of the prevention problem. European Journal of Operational Research, 170(3), 758–770.Google Scholar
  10. Baxter, P., Wilcox, C., McCarthy, M., & Possingham, P. (2007). Optimal management of an annual weed: A stochastic dynamic programming approach. In MODSIM 2007 International Congress on Modeling and Simulation (pp. 2223-2229). Modeling and Simulation Society of Australia and New Zealand.Google Scholar
  11. Baxter, P. W., & Possingham, H. P. (2011). Optimizing search strategies for invasive pests: Learn before you leap. Journal of Applied Ecology, 48(1), 86–95.Google Scholar
  12. Beale, C. M., & Lennon, J. J. (2012). Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B, 367(1586), 247–258.Google Scholar
  13. Begon, M., Mortimer, M., & Thompson, D. J. (2009). Population ecology: A unified study of animals and plants. London: Wiley.Google Scholar
  14. Bellman, R. (1957). A Markovian decision process. DTIC Document.Google Scholar
  15. Berec, L. (2002). Techniques of spatially explicit individual-based models: Construction, simulation, and mean-field analysis. Ecological Modelling, 150(1), 55–81.Google Scholar
  16. Berec, L., Kean, J. M., Epanchin-Niell, R., Liebhold, A. M., & Haight, R. G. (2015). Designing efficient surveys: Spatial arrangement of sample points for detection of invasive species. Biological Invasions, 17(1), 445–459.Google Scholar
  17. Bhat, M. G., & Huffaker, R. G. (2007). Management of a transboundary wildlife population: A self-enforcing cooperative agreement with renegotiation and variable transfer payments. Journal of Environmental Economics and Management, 53(1), 54–67.Google Scholar
  18. Bhat, M. G., Huffaker, R. G., & Lenhart, S. M. (1993). Controlling forest damage by dispersive beaver populations: Centralized optimal management strategy. Ecological Applications, 3(3), 518–530.Google Scholar
  19. Billionnet, A. (2013). Mathematical optimization ideas for biodiversity conservation. European Journal of Operational Research, 231(3), 514–534.Google Scholar
  20. Birch, C. P., Oom, S. P., & Beecham, J. A. (2007). Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecological Modelling, 206(3), 347–359.Google Scholar
  21. Blackburn, T. M., Pyšek, P., Bacher, S., Carlton, J. T., Duncan, R. P., Jarošík, V., et al. (2011). A proposed unified framework for biological invasions. Trends in Ecology & Evolution, 26(7), 333–339.Google Scholar
  22. Blackwood, J., Hastings, A., & Costello, C. (2010). Cost-effective management of invasive species using linear-quadratic control. Ecological Economics, 69(3), 519–527.Google Scholar
  23. Bogich, T., & Shea, K. (2008). A state-dependent model for the optimal management of an invasive metapopulation. Ecological Applications, 18(3), 748–761.Google Scholar
  24. Bogich, T. L., Liebhold, A. M., & Shea, K. (2008). To sample or eradicate? A cost minimization model for monitoring and managing an invasive species. Journal of Applied Ecology, 45(4), 1134–1142.Google Scholar
  25. Born, W., Rauschmayer, F., & Bräuer, I. (2005). Economic evaluation of biological invasions—A survey. Ecological Economics, 55(3), 321–336.Google Scholar
  26. Bowers, J. E., Bean, T. M., & Turner, R. M. (2006). Two decades of change in distribution of exotic plants at the desert laboratory, Tucson, Arizona. Madrono, 53(3), 252–263.Google Scholar
  27. Breukers, A., Mourits, M., van der Werf, W., & Lansink, A. O. (2008). Costs and benefits of controlling quarantine diseases: A bio-economic modeling approach. Agricultural Economics, 38(2), 137–149.Google Scholar
  28. Buhle, E. R., Margolis, M., & Ruesink, J. L. (2005). Bang for buck: Cost-effective control of invasive species with different life histories. Ecological Economics, 52(3), 355–366.Google Scholar
  29. Burnett, K. M., D’evelyn, S., Kaiser, B. A., Nantamanasikarn, P., & Roumasset, J. A. (2008). Beyond the lamppost: Optimal prevention and control of the brown tree snake in Hawaii. Ecological Economics, 67(1), 66–74.Google Scholar
  30. Büyüktahtakin, İ. E. (2011). Dynamic programming via linear programming. In J. J. Cochran Jr., L. A. Cox, P. Keskinocak, J. P. Kharoufeh, & J. C. Smith (Eds.), Wiley encyclopedia of operations research and management science. Hoboken, NJ: Wiley.Google Scholar
  31. Büyüktahtakın, İ. E., & des-Bordes, E., & Kıbış, E. Y., (2017). A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research. Scholar
  32. Büyüktahtakın, İ. E., Feng, Z., Frisvold, G., & Szidarovszky, F. (2011b). A game theoretical approach to invasive species management. Paper presented at the Proceedings of the 2011 industrial engineering research conference, Reno, NV, May, 2011.Google Scholar
  33. Büyüktahtakın, İ. E., Feng, Z., Frisvold, G., & Szidarovszky, F. (2013). Invasive species control based on a cooperative game. Applied Mathematics, 4, 54. Scholar
  34. Büyüktahtakın, İ. E., Feng, Z., Frisvold, G., Szidarovszky, F., & Olsson, A. (2011). A dynamic model of controlling invasive species. Computers & Mathematics with Applications, 62(9), 3326–3333. Scholar
  35. Büyüktahtakin, İ. E., Feng, Z., Olsson, A. D., Frisvold, G., & Szidarovszky, F. (2014b). Invasive species control optimization as a dynamic spatial process: An application to buffelgrass (Pennisetum ciliare) in Arizona. Invasive Plant Science and Management, 7(1), 132–146. Scholar
  36. Büyüktahtakın, İ. E., Feng, Z., & Szidarovszky, F. (2014a). A multi-objective optimization approach for invasive species control. Journal of the Operational Research Society, 65, 1625–1635. Scholar
  37. Büyüktahtakın, İ. E., Kibis, E., Cobuloglu, H. I., Houseman, G. R., & Lampe, J. T. (2015). An age-structured bio-economic model of invasive species management: Insights and strategies for optimal control. Biological Invasions, 17, 2545–2563. Scholar
  38. Cacho, O. J., Hester, S., & Spring, D. (2007). Applying search theory to determine the feasibility of eradicating an invasive population in natural environments. Australian Journal of Agricultural and Resource Economics, 51(4), 425–443.Google Scholar
  39. Cacho, O. J., Spring, D., Hester, S., & Mac Nally, R. (2010). Allocating surveillance effort in the management of invasive species: A spatially-explicit model. Environmental Modelling & Software, 25(4), 444–454.Google Scholar
  40. Caplat, P., Coutts, S., & Buckley, Y. M. (2012). Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants. Annals of the New York Academy of Sciences, 1249(1), 72–83.Google Scholar
  41. Carrasco, L. R., Mumford, J., MacLeod, A., Knight, J., & Baker, R. (2010). Comprehensive bioeconomic modelling of multiple harmful non-indigenous species. Ecological Economics, 69(6), 1303–1312.Google Scholar
  42. Caswell, H. (2001). Matrix population models. Wiley Online Library.Google Scholar
  43. Chadès, I., Martin, T. G., Nicol, S., Burgman, M. A., Possingham, H. P., & Buckley, Y. M. (2011). General rules for managing and surveying networks of pests, diseases, and endangered species. Proceedings of the National Academy of Sciences, 108(20), 8323–8328.Google Scholar
  44. Champ, P., Boyle, K., & Brown, T. (2012). Dordrecht, The Netherlands (Vol. 3). Berlin: Springer.Google Scholar
  45. Chen, C., Epanchin-Niell, R., & Haight, R. G. (2017). Optimal inspection of imports to prevent invasive pest introduction. Risk Analysis,. Scholar
  46. Church, R. L., Murray, A. T., Figueroa, M. A., & Barber, K. H. (2000). Support system development for forest ecosystem management. European Journal of Operational Research, 121(2), 247–258.Google Scholar
  47. Cobuloglu, H. I., & Büyüktahtakın, İ. E. (2017). A two-stage stochastic mixed-integer programming approach to the competition of biofuel and food production. Computers & Industrial Engineering, 107, 251–263.Google Scholar
  48. Cook, D. C. (2008). Benefit cost analysis of an import access request. Food Policy, 33(3), 277–285.Google Scholar
  49. Cushing, J. M. (1998). An introduction to structured population dynamics (Vol. 71). Philadelphia: SIAM.Google Scholar
  50. Dana, E. D., Jeschke, J. M., & García-de-Lomas, J. (2014). Decision tools for managing biological invasions: Existing biases and future needs. Oryx, 48(01), 56–63.Google Scholar
  51. De Roos, A. M., Persson, L., & McCauley, E. (2003). The influence of size-dependent life-history traits on the structure and dynamics of populations and communities. Ecology Letters, 6(5), 473–487.Google Scholar
  52. DeAngelis, D. L., & Mooij, W. M. (2005). Individual-based modeling of ecological and evolutionary processes. Annual Review of Ecology, Evolution, and Systematics, 36, 147–168.Google Scholar
  53. Demon, I., Cunniffe, N., Marchant, B., Gilligan, C., & van den Bosch, F. (2011). Spatial sampling to detect an invasive pathogen outside of an eradication zone. Phytopathology, 101(6), 725–731.Google Scholar
  54. Denyer, D., & Tranfield, D. (2009). Producing a systematic review. In D. A. Buchanan & A. Bryman (Eds.), The SAGE handbook of organizational research methods. London: Sage.Google Scholar
  55. Dolman, P. M., & Wäber, K. (2008). Ecosystem and competition impacts of introduced deer. Wildlife Research, 35(3), 202–214.Google Scholar
  56. Dubey, R., Dubey, R., Gunasekaran, A., Gunasekaran, A., Childe, S. J., Childe, S. J., et al. (2017). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 28(2), 332–362.Google Scholar
  57. Durrett, R., & Levin, S. (1994). The importance of being discrete (and spatial). Theoretical Population Biology, 46(3), 363–394.Google Scholar
  58. Eiswerth, M. E., & Johnson, W. S. (2002). Managing nonindigenous invasive species: Insights from dynamic analysis. Environmental and Resource Economics, 23(3), 319–342.Google Scholar
  59. Elith, J. (2013). Predicting distributions of invasive species. arXiv:1312.0851.
  60. Epanchin-Niell, R. S., Brockerhoff, E. G., Kean, J. M., & Turner, J. A. (2014). Designing cost-efficient surveillance for early detection and control of multiple biological invaders. Ecological Applications, 24(6), 1258–1274.Google Scholar
  61. Epanchin-Niell, R. S., & Liebhold, A. M. (2015). Benefits of invasion prevention: Effect of time lags, spread rates, and damage persistence. Ecological Economics, 116, 146–153.Google Scholar
  62. Epanchin-Niell, R. S., & Wilen, J. E. (2012). Optimal spatial control of biological invasions. Journal of Environmental Economics and Management, 63(2), 260–270.Google Scholar
  63. Epanchin-Niell, R. S., & Wilen, J. E. (2015). Individual and cooperative management of invasive species in human-mediated landscapes. American Journal of Agricultural Economics, 97(1), 180–198.Google Scholar
  64. Epanchin-Niell, R. S., Haight, R. G., Berec, L., Kean, J. M., & Liebhold, A. M. (2012). Optimal surveillance and eradication of invasive species in heterogeneous landscapes. Ecology Letters, 15(8), 803–812.Google Scholar
  65. Epanchin-Niell, R. S., & Hastings, A. (2010). Controlling established invaders: Integrating economics and spread dynamics to determine optimal management. Ecology Letters, 13(4), 528–541.Google Scholar
  66. Finnoff, D., Potapov, A., & Lewis, M. A. (2010). Control and the management of a spreading invader. Resource and Energy Economics, 32(4), 534–550.Google Scholar
  67. Finnoff, D., Shogren, J. F., Leung, B., & Lodge, D. (2007). Take a risk: Preferring prevention over control of biological invaders. Ecological Economics, 62(2), 216–222.Google Scholar
  68. Firn, J., Rout, T., Possingham, H., & Buckley, Y. M. (2008). Managing beyond the invader: Manipulating disturbance of natives simplifies control efforts. Journal of Applied Ecology, 45(4), 1143–1151.Google Scholar
  69. Fisher, R. A. (1937). The wave of advance of advantageous genes. Annals of Eugenics, 7(4), 355–369.Google Scholar
  70. Gandhi, K. J., & Herms, D. A. (2010a). Direct and indirect effects of alien insect herbivores on ecological processes and interactions in forests of eastern North America. Biological Invasions, 12(2), 389–405.Google Scholar
  71. Gandhi, K. J., & Herms, D. A. (2010b). North American arthropods at risk due to widespread Fraxinus mortality caused by the alien emerald ash borer. Biological Invasions, 12(6), 1839–1846.Google Scholar
  72. Gass, S. I. (1983). Decision-aiding models: Validation, assessment, and related issues for policy analysis. Operations Research, 31(4), 603–631.Google Scholar
  73. Getz, W. M., & Haight, R. G. (1989). Population harvesting: Demographic models of fish, forest, and animal resources (Vol. 27). Princeton: Princeton University Press.Google Scholar
  74. Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological models for invasion and persistence of pathogens. Annual Review of Phytopathology, 46, 385–418.Google Scholar
  75. Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603–626.Google Scholar
  76. Green, J. L., Hastings, A., Arzberger, P., Ayala, F. J., Cottingham, K. L., Cuddington, K., et al. (2005). Complexity in ecology and conservation: Mathematical, statistical, and computational challenges. BioScience, 55(6), 501–510.Google Scholar
  77. Gren, I.-M. (2008). Economics of alien invasive species management-choices of targets and policies. Boreal Environment Research, 13, 17–32.Google Scholar
  78. Grimm, V., & Railsback, S. F. (2013). Individual-based modeling and ecology. Princeton: Princeton University Press.Google Scholar
  79. Grimsrud, K. M., Chermak, J. M., Hansen, J., Thacher, J. A., & Krause, K. (2008). A two-agent dynamic model with an invasive weed diffusion externality: An application to yellow starthistle (Centaurea solstitialis L.) in New Mexico. Journal of Environmental Management, 89(4), 322–335.Google Scholar
  80. Gurevitch, J., Fox, G., Wardle, G., & Taub, D. (2011). Emergent insights from the synthesis of conceptual frameworks for biological invasions. Ecology Letters, 14(4), 407–418.Google Scholar
  81. Haight, R. G., Homans, F. R., Horie, T., Mehta, S. V., Smith, D. J., & Venette, R. C. (2011). Assessing the cost of an invasive forest pathogen: A case study with oak wilt. Environmental Management, 47(3), 506–517.Google Scholar
  82. Haight, R. G., & Polasky, S. (2010). Optimal control of an invasive species with imperfect information about the level of infestation. Resource and Energy Economics, 32(4), 519–533.Google Scholar
  83. Hairston, N. G., Smith, F. E., & Slobodkin, L. B. (1960). Community structure, population control, and competition. The American Naturalist, 94(879), 421–425.Google Scholar
  84. Hammonds, J., Hoffman, F., & Bartell, S. (1994). An introductory guide to uncertainty analysis in environmental and health risk assessment. Washington: US DOE.Google Scholar
  85. Hastings, A., Cuddington, K., Davies, K. F., Dugaw, C. J., Elmendorf, S., Freestone, A., et al. (2005). The spatial spread of invasions: New developments in theory and evidence. Ecology Letters, 8(1), 91–101.Google Scholar
  86. Hastings, A., Hall, R. J., & Taylor, C. M. (2006). A simple approach to optimal control of invasive species. Theoretical Population Biology, 70(4), 431–435.Google Scholar
  87. Hauser, C. E., & McCarthy, M. A. (2009). Streamlining ‘search and destroy’: Cost-effective surveillance for invasive species management. Ecology Letters, 12(7), 683–692.Google Scholar
  88. Herms, D. A., & McCullough, D. G. (2014). Emerald ash borer invasion of North America: History, biology, ecology, impacts, and management. Annual Review of Entomology, 59, 13–30.Google Scholar
  89. Hillier, F. S., & Lieberman, G. J. (2012). Introduction to operations research. New York: McGraw-Hill.Google Scholar
  90. Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031–5069.Google Scholar
  91. Hof, J. (1998). Optimizing spatial and dynamic population-based control strategies for invading forest pests. Natural Resource Modeling, 11(3), 197–216.Google Scholar
  92. Hof, J., & Bevers, M. (2000). Direct spatial optimization in natural resource management: Four linear programming examples. Annals of Operations Research, 95(1–4), 67–81.Google Scholar
  93. Hof, J., Bevers, M., & Kent, B. (1997). An optimization approach to area-based forest pest management over time and space. Forest Science, 43(1), 121–128.Google Scholar
  94. Hof, J. G., & Bevers, M. (1998). Spatial optimization for managed ecosystems. New York: Columbia University Press.Google Scholar
  95. Hof, J. G., & Bevers, M. (2002). Spatial optimization in ecological applications. New York: Columbia University Press.Google Scholar
  96. Holmes, T. P., Aukema, J., Englin, J., Haight, R. G., Kovacs, K., & Leung, B. (2014). Economic analysis of biological invasions in forests. In Handbook of forest resource economics. London: Taylor and Francis. Scholar
  97. Holst, N., Rasmussen, I., & Bastiaans, L. (2007). Field weed population dynamics: A review of model approaches and applications. Weed Research, 47(1), 1–14.Google Scholar
  98. Homans, F., & Horie, T. (2011). Optimal detection strategies for an established invasive pest. Ecological Economics, 70(6), 1129–1138.Google Scholar
  99. Horie, T., Haight, R. G., Homans, F. R., & Venette, R. C. (2013). Optimal strategies for the surveillance and control of forest pathogens: A case study with oak wilt. Ecological Economics, 86, 78–85.Google Scholar
  100. Hulme, P. E. (2009). Trade, transport and trouble: Managing invasive species pathways in an era of globalization. Journal of Applied Ecology, 46(1), 10–18.Google Scholar
  101. Hyder, A., Leung, B., & Miao, Z. (2008). Integrating data, biology, and decision models for invasive species management: Application to leafy spurge (Euphorbia esula). Ecology and Society, 13(2), 12.Google Scholar
  102. Hyytiäinen, K., Lehtiniemi, M., Niemi, J. K., & Tikka, K. (2013). An optimization framework for addressing aquatic invasive species. Ecological Economics, 91, 69–79.Google Scholar
  103. Juliano, S. A., & Philip Lounibos, L. (2005). Ecology of invasive mosquitoes: Effects on resident species and on human health. Ecology Letters, 8(5), 558–574.Google Scholar
  104. Kaiser, B. A., & Burnett, K. M. (2010). Spatial economic analysis of early detection and rapid response strategies for an invasive species. Resource and Energy Economics, 32(4), 566–585.Google Scholar
  105. Kantas, A. B., Cobuloglu, H. I., & Büyüktahtakın, İ. E. (2015). Multi-source capacitated lot-sizing for economically viable and clean biofuel production. Journal of Cleaner Production, 94, 116–129.Google Scholar
  106. Kellerer, H., Pferschy, U., & Pisinger, D. (2004). Introduction to NP-completeness of knapsack problems. Berlin: Springer.Google Scholar
  107. Kennedy, J. O. (1981). Applications of dynamic programming to agriculture, forestry and fisheries: Review and prognosis. Melbourne: La Trobe University.Google Scholar
  108. Kibis, E. Y., & Büyüktahtakın, İ. E. (2014). Simulating treatment strategies for invasive species control under dispersal uncertainty. Paper presented at the Proceedings of the international conference on agriculture, environment and biological sciences, Antalya, Turkey.Google Scholar
  109. Kibis, E. Y., & Büyüktahtakın, İ. E. (2017). Optimizing invasive species management: A mixed-integer linear programming approach. European Journal of Operational Research, 259(1), 308–321.Google Scholar
  110. Kot, M., & Schaffer, W. M. (1986). Discrete-time growth-dispersal models. Mathematical Biosciences, 80(1), 109–136.Google Scholar
  111. Kovacs, K. F., Haight, R. G., McCullough, D. G., Mercader, R. J., Siegert, N. W., & Liebhold, A. M. (2010). Cost of potential emerald ash borer damage in US communities, 2009–2019. Ecological Economics, 69(3), 569–578.Google Scholar
  112. Kovacs, K. F., Haight, R. G., Mercader, R. J., & McCullough, D. G. (2014). A bioeconomic analysis of an emerald ash borer invasion of an urban forest with multiple jurisdictions. Resource and Energy Economics, 36(1), 270–289.Google Scholar
  113. Lee, E. K., Maheshwary, S., Mason, J., & Glisson, W. (2006). Large-scale dispensing for emergency response to bioterrorism and infectious-disease outbreak. Interfaces, 36(6), 591–607.Google Scholar
  114. Leung, B., Lodge, D. M., Finnoff, D., Shogren, J. F., Lewis, M. A., & Lamberti, G. (2002). An ounce of prevention or a pound of cure: Bioeconomic risk analysis of invasive species. Proceedings of the Royal Society of London B: Biological Sciences, 269(1508), 2407–2413.Google Scholar
  115. Levine, J. M., Vila, M., Antonio, C. M., Dukes, J. S., Grigulis, K., & Lavorel, S. (2003). Mechanisms underlying the impacts of exotic plant invasions. Proceedings of the Royal Society of London. Series B: Biological Sciences, 270(1517), 775–781.Google Scholar
  116. Liebhold, A., & Bascompte, J. (2003). The Allee effect, stochastic dynamics and the eradication of alien species. Ecology Letters, 6(2), 133–140.Google Scholar
  117. Littman, M. L. (2009). A tutorial on partially observable Markov decision processes. Journal of Mathematical Psychology, 53(3), 119–125.Google Scholar
  118. Lockwood, J. L., Cassey, P., & Blackburn, T. (2005). The role of propagule pressure in explaining species invasions. Trends in Ecology & Evolution, 20(5), 223–228.Google Scholar
  119. Maass, A., Hufschmidt, M. M., Dorfman, R., Thomas, H. A., Marglin, S. A., Fair, G. M., et al. (1962). Design of water-resource systems. Cambridge, MA: Harvard University Press.Google Scholar
  120. Mangel, M., & Clark, C. W. (1988). Dynamic modeling in behavioral ecology. Princeton: Princeton University Press.Google Scholar
  121. Mbah, M. L. N., & Gilligan, C. A. (2010). Balancing detection and eradication for control of epidemics: Sudden oak death in mixed-species stands. PLoS ONE, 5(9), e12317.Google Scholar
  122. McDonald, C., & McPherson, G. (2013). Creating hotter fires in the Sonoran Desert: Buffelgrass produces copious fuels and high fire temperatures. Fire Ecology, 9(2), 26–39.Google Scholar
  123. Mehta, S. V., Haight, R. G., Homans, F. R., Polasky, S., & Venette, R. C. (2007). Optimal detection and control strategies for invasive species management. Ecological Economics, 61(2), 237–245.Google Scholar
  124. Menge, B. A., & Sutherland, J. P. (1987). Community regulation: Variation in disturbance, competition, and predation in relation to environmental stress and recruitment. The American Naturalist, 130(5), 730–757.Google Scholar
  125. Meyer, P. S., Yung, J. W., & Ausubel, J. H. (1999). A primer on logistic growth and substitution: The mathematics of the Loglet Lab software. Technological Forecasting and Social Change, 61(3), 247–271.Google Scholar
  126. Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being. Washington, DC.Google Scholar
  127. Monahan, G. E. (1982). State of the art—A survey of partially observable Markov decision processes: theory, models, and algorithms. Management Science, 28(1), 1–16.Google Scholar
  128. Moore, A. L., & McCarthy, M. A. (2016). Optimizing ecological survey effort over space and time. Methods in Ecology and Evolution, 7(8), 891–899.Google Scholar
  129. Moore, J. L., Rout, T. M., Hauser, C. E., Moro, D., Jones, M., Wilcox, C., et al. (2010). Protecting islands from pest invasion: Optimal allocation of biosecurity resources between quarantine and surveillance. Biological Conservation, 143(5), 1068–1078.Google Scholar
  130. Moore, J. L., Runge, M. C., Webber, B. L., & Wilson, J. R. (2011). Contain or eradicate? Optimizing the management goal for Australian acacia invasions in the face of uncertainty. Diversity and Distributions, 17(5), 1047–1059.Google Scholar
  131. Nemhauser, G. L., Savelsbergh, M. W. P., & Sigismondi, G. S. (1992). Constraint classification for mixed integer programming formulations. COAL Bulletin, 20, 8–12.Google Scholar
  132. Nemhauser, G. L., & Wolsey, L. A. (1988a). Integer and combinatorial optimization (Vol. 18). New York: Wiley.Google Scholar
  133. Nemhauser, G. L., & Wolsey, L. A. (1988b). Integer programming and combinatorial optimization. Chichester: Wiley.Google Scholar
  134. Neubert, M. G., & Caswell, H. (2000). Demography and dispersal: Calculation and sensitivity analysis of invasion speed for structured populations. Ecology, 81(6), 1613–1628.Google Scholar
  135. Nicol, S., & Chadès, I. (2011). Beyond stochastic dynamic programming: A heuristic sampling method for optimizing conservation decisions in very large state spaces. Methods in Ecology and Evolution, 2(2), 221–228.Google Scholar
  136. NISC. (2001). Meeting the invasive species challenge: National invasive species management plan. Washington: National Invasive Species Council.Google Scholar
  137. Olson, L. J. (2006). The economics of terrestrial invasive species: A review of the literature. Agricultural and Resource Economics Review, 35(1), 178.Google Scholar
  138. Olson, L. J., & Roy, S. (2005). On prevention and control of an uncertain biological invasion. Applied Economic Perspectives and Policy, 27(3), 491–497.Google Scholar
  139. Pacala, S. W., & Silander, J. (1990). Field tests of neighborhood population dynamic models of two annual weed species. Ecological Monographs, 60(1), 113–134.Google Scholar
  140. Pejchar, L., & Mooney, H. A. (2009). Invasive species, ecosystem services and human well-being. Trends in Ecology & Evolution, 24(9), 497–504.Google Scholar
  141. Perrings, C., Dalmazzone, S., & Williamson, M. H. (2000). The economics of biological invasions. Broadheath: Edward Elgar Publishing.Google Scholar
  142. Pichancourt, J. B., Chadès, I., Firn, J., van Klinken, R. D., & Martin, T. G. (2012). Simple rules to contain an invasive species with a complex life cycle and high dispersal capacity. Journal of Applied Ecology, 49(1), 52–62.Google Scholar
  143. Pimentel, D. (2011). Biological invasions: Economic and environmental costs of alien plant, animal, and microbe species. Boca Raton: CRC Press.Google Scholar
  144. Pimentel, D., Zuniga, R., & Morrison, D. (2005). Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecological Economics, 52(3), 273–288.Google Scholar
  145. Polasky, S. (2010). A model of prevention, detection, and control for invasive species (pp. 100–107). Oxford: Oxford University Press.Google Scholar
  146. Polasky, S., & Segerson, K. (2009). Integrating ecology and economics in the study of ecosystem services: Some lessons learned. Annual Review of Resource Economics, 1, 409–434.Google Scholar
  147. Potapov, A. (2009). Stochastic model of lake system invasion and its optimal control: Neurodynamic programming as a solution method. Natural Resource Modeling, 22(2), 257–288.Google Scholar
  148. Potapov, A., & Lewis, M. (2008). Allee effect and control of lake system invasion. Bulletin of Mathematical Biology, 70(5), 1371–1397.Google Scholar
  149. Pullin, A. S., & Stewart, G. B. (2006). Guidelines for systematic review in conservation and environmental management. Conservation Biology, 20(6), 1647–1656.Google Scholar
  150. Quick, Z., Houseman, G., & Büyüktahtakin, İ. E. (2017). Assessing wind and mammals as seed dispersal vectors in an invasive legume. Weed Research, 57(1), 35–43.Google Scholar
  151. Rabbinge, R., & Rossing, W. (1987). Decision models in pest management. European Journal of Operational Research, 32(3), 327–332.Google Scholar
  152. Ramula, S., Knight, T. M., Burns, J. H., & Buckley, Y. M. (2008). General guidelines for invasive plant management based on comparative demography of invasive and native plant populations. Journal of Applied Ecology, 45(4), 1124–1133.Google Scholar
  153. Regan, T. J., McCarthy, M. A., Baxter, P. W., Dane Panetta, F., & Possingham, H. P. (2006). Optimal eradication: When to stop looking for an invasive plant. Ecology Letters, 9(7), 759–766.Google Scholar
  154. Rejmánek, M., & Pitcairn, M. (2002). When is eradication of exotic pest plants a realistic goal. In Turning the tide: The eradication of invasive species (pp. 249–253).Google Scholar
  155. Richardson, D. M., Pyšek, P., Rejmánek, M., Barbour, M. G., Panetta, F. D., & West, C. J. (2000). Naturalization and invasion of alien plants: Concepts and definitions. Diversity and Distributions, 6(2), 93–107.Google Scholar
  156. Rivington, M., Matthews, K., Bellocchi, G., & Buchan, K. (2006). Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data. Agricultural Systems, 88(2), 451–471.Google Scholar
  157. Rout, T. M., Moore, J. L., & McCarthy, M. A. (2014). Prevent, search or destroy? A partially observable model for invasive species management. Journal of Applied Ecology, 51(3), 804–813.Google Scholar
  158. Rout, T. M., Moore, J. L., Possingham, H. P., & McCarthy, M. A. (2011). Allocating biosecurity resources between preventing, detecting, and eradicating island invasions. Ecological Economics, 71, 54–62.Google Scholar
  159. Sanchirico, J. N., Albers, H. J., Fischer, C., & Coleman, C. (2010). Spatial management of invasive species: Pathways and policy options. Environmental and Resource Economics, 45(4), 517–535.Google Scholar
  160. Scheller, R. M., & Mladenoff, D. J. (2007). An ecological classification of forest landscape simulation models: Tools and strategies for understanding broad-scale forested ecosystems. Landscape Ecology, 22(4), 491–505.Google Scholar
  161. Sebert-Cuvillier, E., Simon-Goyheneche, V., Paccaut, F., Chabrerie, O., Goubet, O., & Decocq, G. (2008). Spatial spread of an alien tree species in a heterogeneous forest landscape: A spatially realistic simulation model. Landscape Ecology, 23(7), 787–801.Google Scholar
  162. Sharov, A. A., & Liebhold, A. M. (1998). Model of slowing the spread of gypsy moth (Lepidoptera: Lymantriidae) with a barrier zone. Ecological Applications, 8(4), 1170–1179.Google Scholar
  163. Simberloff, D., Martin, J.-L., Genovesi, P., Maris, V., Wardle, D. A., Aronson, J., et al. (2013). Impacts of biological invasions: What’s what and the way forward. Trends in Ecology & Evolution, 28(1), 58–66.Google Scholar
  164. Skarpaas, O., Shea, K., & Bullock, J. M. (2005). Optimizing dispersal study design by Monte Carlo simulation. Journal of Applied Ecology, 42(4), 731–739.Google Scholar
  165. Skellam, J. G. (1951). Random dispersal in theoretical populations. Biometrika, 38(1/2), 196–218.Google Scholar
  166. Soliman, T., Mourits, M. C., Van Der Werf, W., Hengeveld, G. M., Robinet, C., & Lansink, A. G. O. (2012). Framework for modelling economic impacts of invasive species, applied to pine wood nematode in Europe. PLoS ONE, 7(9), e45505.Google Scholar
  167. Southern Arizona Buffelgrass Coordination Center. (2011). Buffelgrass invasion in the Sonoran Desert: Imminent risks and unavoidable mitigation.Google Scholar
  168. Springborn, M. R. (2014). Risk aversion and adaptive management: Insights from a multi-armed bandit model of invasive species risk. Journal of Environmental Economics and Management, 68(2), 226–242.Google Scholar
  169. Surkov, I. V., Lansink, A. G. O., & Van der Werf, W. (2009). The optimal amount and allocation of sampling effort for plant health inspection. European Review of Agricultural Economics, 36, 295–320.Google Scholar
  170. Szidarovszky, F., Gershon, M. E., & Duckstein, L. (1986). Techniques for multiobjective decision making in systems management (Vol. 2). Amsterdam: Elsevier.Google Scholar
  171. Tanner, J. E. (1999). Density-dependent population dynamics in clonal organisms: A modelling approach. Journal of Animal Ecology, 68(2), 390–399.Google Scholar
  172. Taylor, C. M., & Hastings, A. (2004). Finding optimal control strategies for invasive species: A density-structured model for Spartina alterniflora. Journal of Applied Ecology, 41(6), 1049–1057.Google Scholar
  173. Taylor, C. M., & Hastings, A. (2005). Allee effects in biological invasions. Ecology Letters, 8(8), 895–908.Google Scholar
  174. Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European Journal of Operational Research, 94(1), 1–15.Google Scholar
  175. Tilman, D. (2004). Niche tradeoffs, neutrality, and community structure: A stochastic theory of resource competition, invasion, and community assembly. Proceedings of the National Academy of Sciences of the United States of America, 101(30), 10854–10861.Google Scholar
  176. Tjørve, E. (2009). Shapes and functions of species-area curves (II): A review of new models and parameterizations. Journal of Biogeography, 36(8), 1435–1445.Google Scholar
  177. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.Google Scholar
  178. Tyre, A. J., Possingham, H. P., & Lindenmayer, D. B. (1998). Modelling dispersal behaviour on a fractal landscape. Environmental Modelling & Software, 14(1), 103–113.Google Scholar
  179. U.S. Department of Interiror (1999). National Invasive Species Council.
  180. Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A., & Losos, E. (1998). Quantifying threats to imperiled species in the United States. BioScience, 48, 607–615.Google Scholar
  181. Williamson, M. (1996). Biological invasions (Vol. 15). Berlin: Springer.Google Scholar
  182. With, K. A. (2002). The landscape ecology of invasive spread. Conservation Biology, 16(5), 1192–1203.Google Scholar
  183. Wong, C. Y., Wong, C. W., & Boon-Itt, S. (2015). Integrating environmental management into supply chains: A systematic literature review and theoretical framework. International Journal of Physical Distribution & Logistics Management, 45(1/2), 43–68.Google Scholar
  184. Yakob, L., Kiss, I. Z., & Bonsall, M. B. (2008). A network approach to modeling population aggregation and genetic control of pest insects. Theoretical Population Biology, 74(4), 324–331.Google Scholar
  185. Yamamura, K., Katsumata, H., Yoshioka, J., Yuda, T., & Kasugai, K. (2016). Sampling inspection to prevent the invasion of alien pests: Statistical theory of import plant quarantine systems in Japan. Population Ecology, 58(1), 63–80.Google Scholar
  186. Yeh, W. W. G. (1985). Reservoir management and operations models: A state-of-the-art review. Water Resources Research, 21(12), 1797–1818.Google Scholar
  187. Yemshanov, D., Haight, R. G., Koch, F. H., Lu, B., Venette, R., Fournier, R. E., et al. (2017). Robust surveillance and control of invasive species using a scenario optimization approach. Ecological Economics, 133, 86–98.Google Scholar
  188. Yemshanov, D., Haight, R. G., Koch, F. H., Lu, B., Venette, R., Lyons, D. B., et al. (2015). Optimal allocation of invasive species surveillance with the maximum expected coverage concept. Diversity and Distributions, 21(11), 1349–1359.Google Scholar
  189. Yokomizo, H., Possingham, H. P., Thomas, M. B., & Buckley, Y. M. (2009). Managing the impact of invasive species: The value of knowing the density–impact curve. Ecological Applications, 19(2), 376–386.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial Engineering, Mechanical Engineering CenterNew Jersey Institute of TechnologyNewarkUSA
  2. 2.USDA Forest Service Northern Research StationSt. PaulUSA

Personalised recommendations