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An age-structured bio-economic model of invasive species management: insights and strategies for optimal control

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Abstract

Controlling invasive species is a highly complex problem defined by the biological characteristics of the organisms, the landscape context, and a management objective of minimizing invasion damages given limited financial resources. While bio-economic optimization models provide a promising approach for invasive species control, current spatio-temporal optimization models omit key ecological details such as age structures—which could be essential to predict how populations grow and spread spatially over time and determine the most effective control strategies. We develop a novel age-structured optimization model as a spatial-dynamic decision framework for controlling invasive species. In particular, we propose a new carrying capacity sub-model, which allows us to take into account the biological competition among different age classes within the population. The potential use of the model is demonstrated on controlling the invasion of sericea (Lespedeza cuneata), a perennial legume threatening native grasslands in the Great Plains. The results show that incorporating age-structure into the model captures important biological characteristics of the species and leads to unexpected results such as multi-logistic population growth with multiple, sequential, and overlapping phases of logistic form. These new findings can contribute to understanding time-lags and invasion growth dynamics. Additionally, given budget constraints, utilizing control measures every 2–3 years is found to be more effective than yearly control because of the time to reproductive maturity. Results of the bio-economic optimization approach provide both ecological and economic insights into the control of invasive species. Furthermore, while the proposed model is specific enough to capture biological realism, it also has the potential to be generalized to a wide range of invasive plant and animal species under various management scenarios in order to identify the most efficient control strategies for managing invasive species.

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References

  • Aikio S, Duncan RP, Hulme PE (2010) Lag-phases in alien plant invasions: separating the facts from the artifacts. Oikos 119(2):370–378

    Article  Google Scholar 

  • Albers HJ, Fischer C, Sanchirico JN (2010) Invasive species management in a spatially heterogeneous world: effects of uniform policies. Resour Energy Econ 32:483–499

    Article  Google Scholar 

  • Bhat MG, Huffaker RG, Lenhart SM (1993) Controlling forest damage by dispersive beaver populations: centralized optimal management strategy. Ecol Appl 3(3):518–530

    Article  Google Scholar 

  • Billionnet A (2013) Mathematical optimization ideas for biodiversity conservation. Eur J Oper Res 231:514–534

    Article  Google Scholar 

  • Blackwood J, Hastings A, Costello C (2010) Cost-effective management of invasive species using linear-quadratic control. Ecol Econ 69:519–527

    Article  Google Scholar 

  • Büyüktahtakın İE, Feng Z, Frisvold G, Szidarovszky F, Olsson A (2011) A dynamic model of controlling invasive species. Comput Math Appl 62:3326–3333

    Article  Google Scholar 

  • Büyüktahtakın İE, Feng Z, Frisvold G, Szidarovszky F (2013) Invasive species control based on a cooperative game. Appl Math 4(10):54

    Article  Google Scholar 

  • Caswell H (2001) Matrix population models: construction, analysis, and interpretation, 2nd edn. Sinauer Associates, Sunderland

    Google Scholar 

  • Clark C (1990) Mathematical bioeconomics: the optimal management of renewable resources, 2nd edn. Wiley, New York

    Google Scholar 

  • Cook LH (1965) Oscillation in the simple logistic growth model. Nature 207:316

    Article  Google Scholar 

  • Czyzyk J, Mesnier M, Moré J (1998) The NEOS server. IEEE J Comput Sci Eng 5(3):68–75

    Article  Google Scholar 

  • Drud A (1985) CONOPT: a GRG code for large sparse dynamic nonlinear optimization problems. Math Program 31(2):153–191

    Article  Google Scholar 

  • Duncan CA, Jachetta JJ, Brown ML, Carrithers VF, Clark JK, DiTomaso JM et al (2004) Assessing the economic, environmental, and societal losses from invasive plants on rangeland and wildlands. Weed Technol 18:1411–1416

    Article  Google Scholar 

  • Epanchin-Niell R, Hastings A (2010) Controlling established invaders: integrating economics and spread dynamics to determine optimal management. Ecol Lett 13(4):528–541

    Article  PubMed  Google Scholar 

  • Epanchin-Niell RS, Wilen J (2012) Optimal spatial control of biological invasions. J Environ Econ Manag 63(2):260–270

    Article  Google Scholar 

  • Fazekas J, Kadar F, Sarospataki M, Lovei GL (1997) Seasonal activity age structure and egg production of the ground beetle Anisodactylus signatus (Coleoptera: Carabidae) in Hungary. Eur J Entomol 94:473–484

    Google Scholar 

  • Fokas N (2007) Growth functions, social diffusion, and social change. Rev Sociol 13(1):5–30

    Article  Google Scholar 

  • Forgó F, Szép J, Szidarovszky F (1999) Introduction to the theory of games. Concepts, methods, applications. Kluwer, Dordrecht

    Google Scholar 

  • Fechter RH, Jones R (2001) Estimated economic impacts of the invasive plant sericea lespedeza on Kansas grazing lands. J Agric Appl Econ 33:630

  • Fourer R, Gay DM, Kernighan BW (2003) AMPL: a modeling language for mathematical programming, Duxbury Press, Brooks/Cole-Thomson Publishing Company, Pacific Grove, CA

  • Getz WM, Haight RG (1989) Population harvesting: Demographic models of fish, forest and animal resources Monographs in Population Biology, vol 27. Princeton University Press, Princeton

  • Gurevitch J, Fox GA et al (2011) Emergent insights from the synthesis of conceptual frameworks for biological invasions. Ecol Lett 14(4):407–418

    Article  CAS  PubMed  Google Scholar 

  • Hof J (1998) Optimizing spatial and dynamic population-based control strategies for invading forest pests. Nat Resour Model 11:197–216

    Google Scholar 

  • Hof J, Bevers M (2002) Spatial optimization in ecological applications. Columbia University Press, New York

    Google Scholar 

  • Houseman G, Foster B, Brassil CE (2014) Propagule pressure-invasibility relationships: testing the influence of soil fertility and disturbance with Lespedeza cuneata. Oecologia 174(2):511–520

    Article  PubMed  Google Scholar 

  • Kaiser BA, Burnett KM (2010) Spatial economic analysis of early detection and rapid response strategies for an invasive species. Resour Energy Econ 32:566–585

    Article  Google Scholar 

  • Kansas State University (2012) 2012 Chemical weed control, SRP1063. http://www.atchison.ksu.edu/doc39521.ashx. Accessed 19 Jan 2014

  • Koji S, Nakamura K (2006) Seasonal fluctuation, age structure, and annual changes in a population of Cassida rubiginosa (Coleoptera: Chrysomelidae) in a natural habitat. Ann Entomol Soc Am 99:292–299

    Article  Google Scholar 

  • Kovacs KF, Haight RG, Mercader RJ, McCullough DG (2014) A bioeconomic analysis of an emerald ash borer invasion of an urban forest with multiple jurisdictions. Resour Energy Econ 36:270–289

    Article  Google Scholar 

  • Kucharavy D, De Guio R (2011) Logistic substitution model and technological forecasting. Procedia Eng 9:402–416

    Article  Google Scholar 

  • Lance TV, Terrence GB, Stritzke J (1997) Ecology and management of Sericea Lespedeza. http://pods.dasnr.okstate.edu/docushare/dsweb/Get/Rendition-7591/PSS-2874web+color.pdf. Accessed 19 Jan 2014

  • Leslie PH (1945) The use of matrices in certain population mathematics. Biometrika 33(3):183–212

    Article  CAS  PubMed  Google Scholar 

  • Meyer PS (1994) Bi-logistic growth. Technol Forecast Soc Change 47:89–102

    Article  Google Scholar 

  • Meyer PS, Yung JW, Ausubel JH (1999) A Primer on logistic growth and substitution: the mathematics of the Loglet lab softwar. Technol Forecast Soc Change 61(3):247–271

    Article  Google Scholar 

  • Ohlenbusch DP, Bidwell T, Fick H, Scott W, Clubine S, Coffin M et al (2007) Sericea Lespedeza: history, characteristics, and identification. Kansas State University Agricultural Experiment Station, Cooperative Extension Service, Manhattan

    Google Scholar 

  • Olson LJ (2006) The economics of terrestrial invasive species: a review of the literature. Agric Resour Econ Rev 35(1):178–194

    Google Scholar 

  • Pysek P, Hulme PE (2005) Spatio-temporal dynamics of plant invasions: linking pattern to process. Ecoscience 12(3):302–315

    Article  Google Scholar 

  • Schreiber SJ, Lloyd-Smith JO (2009) Invasion dynamics in spatially heterogeneous environments. Am Nat 174(4):490–505

    Article  PubMed  Google Scholar 

  • Schutzenhofer MR, Knight TM (2007) Population-level effects of augmented herbivory on Lespedeza cuneata: implications for biological control. Ecol Appl 17(4):965–971

    Article  PubMed  Google Scholar 

  • Schutzenhofer M, Valone T, Knight T (2009) Herbivory and population dynamics of invasive and native lespedeza. Oecologia 161(1):57–66

    Article  PubMed  Google Scholar 

  • Shelton A, Munch S, Keith D, Mangel M (2012) Maternal age, fecundity, egg quality, and recruitment: linking stock structure to recruitment using an age-structured Ricker model. Can J Fish Aquat Sci 69:1631–1641

    Article  Google Scholar 

  • Stone R (1980) Sigmoids. Bull Appl Stat 7:59–119

    Article  Google Scholar 

  • Tahvonen O (2008) Harvesting age-structured populations as a biomass. Does it work? Nat Resour Model 21:525–550

    Article  Google Scholar 

  • Taylor C, Hastings A (2004) Finding optimal control strategies for an invasive grass using a density-structured model. J Appl Ecol 41:1049–1057

    Article  Google Scholar 

  • Trappey CV, Wu HY (2008) An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles. Adv Eng Inform 22(4):421–430

    Article  Google Scholar 

  • With KA (2002) The landscape ecology of invasive spread. Conserv Biol 16(5):1192–1203

    Article  Google Scholar 

  • Woods MT, Hartnett CD, Ferguson JC (2009) High propagule production and reproductive fitness homeostasis contribute to the invasiveness of Lespedeza cuneata (Fabaceae). Biol Invasions 11(8):1913–1927

    Article  Google Scholar 

  • Wu J (2001) Optimal weed control under static and dynamic decision rules. Agric Econ 25(1):119–130

    Article  Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge the support of the National Science Foundation under Grant No. EPS-0903806, the state of Kansas through the Kansas Board of Regents, and the Strategic Engineering Research Fellowship (SERF) of the College of Engineering at Wichita State University. We also thank two anonymous referees and the associate editor whose remarks helped to improve the clarity of our exposition.

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Correspondence to İ. Esra Büyüktahtakın.

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Büyüktahtakın, İ.E., Kıbış, E.Y., Cobuloglu, H.I. et al. An age-structured bio-economic model of invasive species management: insights and strategies for optimal control. Biol Invasions 17, 2545–2563 (2015). https://doi.org/10.1007/s10530-015-0893-4

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  • DOI: https://doi.org/10.1007/s10530-015-0893-4

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