Skip to main content

Advertisement

SpringerLink
Log in

Using stylized agent-based models for population–environment research: a case study from the Galápagos Islands

  • Original Paper
  • Published:
Population and Environment Aims and scope Submit manuscript

Abstract

Agent-based models (ABMs) are powerful tools for population–environment research but are subject to trade-offs between model complexity and abstraction. This study strikes a compromise between abstract and highly specified ABMs by designing a spatially explicit, stylized ABM and using it to explore policy scenarios in a setting that is facing substantial conservation and development challenges. Specifically, we present an ABM that reflects key Land Use/Land Cover dynamics and livelihood decisions on Isabela Island in the Galápagos Archipelago of Ecuador. We implement the model using the NetLogo software platform, a free program that requires relatively little programming experience. The landscape is composed of a satellite-derived distribution of a problematic invasive species (common guava) and a stylized representation of the Galápagos National Park, the community of Puerto Villamil, the agricultural zone, and the marine area. The agent module is based on publicly available data and household interviews and represents the primary livelihoods of the population in the Galápagos Islands—tourism, fisheries, and agriculture. We use the model to enact hypothetical agricultural subsidy scenarios aimed at controlling invasive guava and assess the resulting population and land cover dynamics. Findings suggest that spatially explicit, stylized ABMs have considerable utility, particularly during preliminary stages of research, as platforms for (1) sharpening conceptualizations of population–environment systems, (2) testing alternative scenarios, and (3) uncovering critical data gaps.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abel, T., & Stepp, J. R. (2003). A new ecosystems ecology for anthropology. Conservation Ecology, 7(3), 12.

    Google Scholar 

  • Acevedo, M. F., Callicott, J. B., Monticino, M., Lyons, D., Palomino, J., Rosales, J., et al. (2008). Models of natural and human dynamics in forest landscapes: Cross-site and cross-cultural synthesis. Geoforum, 39(2), 846–866.

    Article  Google Scholar 

  • Acosta-Michlik, L., & Espaldon, V. (2008). Assessing vulnerability of selected farming communities in the Philippines based on a behavioural model of agent’s adaptation to global environmental change. Global Environmental Change-Human and Policy Dimensions, 18(4), 554–563.

    Google Scholar 

  • An, G. (2001). Agent-based computer simulation and SIRS: Building a bridge between basic science and clinical trials. Shock, 16(4), 266–273.

    Article  Google Scholar 

  • An, L., Linderman, M., Qi, J., Shortridge, A., & Liu, J. (2005). Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers, 95(1), 54–79.

    Article  Google Scholar 

  • Anderson, P. W., Arrow, K. J., & Pines, D. (Eds.). (1988). The economy as an evolving complex system. Boulder, CO: Westview Press.

    Google Scholar 

  • Arthur, W. B. (1999). Complexity and the economy. Science, 284(5411), 107–109.

    Article  Google Scholar 

  • Auchincloss, A. H., & Diez Roux, A. V. (2008). A new tool for epidemiology: The usefulness of dynamic-agent models in understanding place effects on health. American Journal of Epidemiology, 168(1), 1–8.

    Article  Google Scholar 

  • Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Batty, M., & Torrens, P. M. (2005). Modelling and prediction in a complex world. Futures, 37(7), 745–766.

    Article  Google Scholar 

  • Beinhocker, E. D. (2006). The origin of wealth: Evolution, complexity, and the radical remaking of economics. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • Blackman, T. (2000). Complexity theory. In G. Browning, A. Haleli, & A. Webster (Eds.), Understanding complexity society: Theories of the present. London: Sage Publications.

    Google Scholar 

  • Bolte, J. P., Hulse, D. W., Gregory, S. V., & Smith, C. (2007). Modeling biocomplexity—Actors, landscapes and alternative futures. Environmental Modelling & Software, 22(5), 570–579.

    Article  Google Scholar 

  • Brown, D. G., Robinson, D. T., An, L., Nassauer, J. I., Zellner, M., Rand, W., et al. (2008). Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system. Geoforum, 39(2), 805–818.

    Article  Google Scholar 

  • Collier, N. (2003). Repast: An extensible framework for agent simulation. Chicago, IL: The University of Chicago, Social Science Research Computing.

    Google Scholar 

  • Davis, K. (1990). Population and resources: Fact and interpretation. Population and Development Review, 16, 1–21.

    Article  Google Scholar 

  • Deadman, P., Robinson, D., Moran, E., & Brondizio, E. (2004). Colonist household decision-making and land-use change in the Amazon rainforest: An agent-based simulation. Environment and Planning B-Planning & Design, 31(5), 693–709.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Cambridge, MA: MIT Press.

    Google Scholar 

  • Erlien, C. M., Tuttle, J. P., McCleary, A. L., Mena, C. F., & Walsh, S. J. (2006). Complexity theory and spatial simulations of land use/land cover dynamics: The use of “what if” scenarios for education, land management, and decision-making. Geocarto International, 21(4), 67–74.

    Article  Google Scholar 

  • Evans, T. P., & Kelley, H. (2004). Multi-scale analysis of a household level agent-based model of landcover change. Journal of Environmental Management, 72(1–2), 57–72.

    Article  Google Scholar 

  • Evans, T. P., & Kelley, H. (2008). Assessing the transition from deforestation to forest regrowth with an agent-based model of land cover change for south-central Indiana (USA). Geoforum, 39(2), 819–832.

    Article  Google Scholar 

  • Farmer, J. D., Packard, N. H., & Perelson, A. S. (1986). The immune system, adaptation, and machine learning. Physica D, 2, 187–204.

    Article  Google Scholar 

  • Galvin, K., Thornton, P. K., Roque de Pinho, J., Sunderland, J., & Boone, R. B. (2006). Integrated modeling and its potential for resolving conflicts between conservation and people in the rangelands of East Africa. Human Ecology, 34(2), 155–181.

    Article  Google Scholar 

  • Gonzalez, J. A., Montes, C., Rodriguez, J., & Tapia, W. (2008). Rethinking the Galápagos Islands as a complex social-ecological system: Implications for conservation and management. Ecology and Society, 13(2), 13. Accessed April 2009. http://www.ecologyandsociety.org/vol13/iss2/art13/.

  • Grenier, C. (2007). Conservación contra natura: Las Islas Galápagos. Quito: Ediciones Abya-Yala.

    Google Scholar 

  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., et al. (2005). Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science, 310(5750), 987–991.

    Article  Google Scholar 

  • Hamann, O. (1981). Plant communities of the Galápagos islands. Dansk Botanisk Arkiv, 34(2), 1–163.

    Google Scholar 

  • Hamilton, K., & Clemens, M. (1999). Genuine savings rates in developing countries. World Bank Economic Review, 13(2), 333–356.

    Google Scholar 

  • Hare, M., & Deadman, P. (2004). Further towards a taxonomy of agent-based simulation models in environmental management. Mathematics and Computers in Simulation, 64(1), 25–40.

    Article  Google Scholar 

  • Holland, J. H. (1996). Hidden order: How adaptation builds complexity. New York, NY: Perseus Books.

    Google Scholar 

  • Janssen, M. (1998). Use of complex adaptive systems for the modeling of global change. Ecosystems, 1(5), 57–63.

    Article  Google Scholar 

  • Jørgensen, S. E. (1986). Fundamentals of ecological modelling. Amsterdam: Elsevier.

    Google Scholar 

  • Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. New York, NY: Oxford University Press.

    Google Scholar 

  • Kerr, S., Cardenas, S., & Hendy, J. (2004). Migration and the environment in the Galápagos: An analysis of economic and policy incentives driving migration, potential impacts from migration control, and potential policies to reduce migration pressure (Working Paper). Wellington, New Zealand: Motu Economic and Public Policy Research.

  • Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28, 205–241.

    Article  Google Scholar 

  • Le, Q. B., Park, S. J., Vlek, P. L. G., & Cremers, A. B. (2008). Land-use dynamic simulator (LUDAS): A multi-agent system model for simulating spatio-temporal dynamics of coupled human-landscape system. Ecological Informatics, 3(2), 135–153.

    Article  Google Scholar 

  • Little, L. R., Kuikka, S., Punt, A. E., Pantus, F., Davies, C. R., & Mapstone, B. D. (2004). Information flow among fishing vessels modelled using a bayesian network. Environmental Modelling & Software, 19(1), 27–34.

    Article  Google Scholar 

  • Luna, M. A. (2008). Value of the basic and essential family baskets in Galápagos. In L. J. Cayot & M. V. Toral Granda (Eds.), Galápagos report 2007–2008. Puerto Ayora, Galápagos: CDF, GNP and INGALA.

    Google Scholar 

  • Lutz, W., Prskawetz, A., & Sanderson, W. C. (2002). Introduction. Population and Development Review, 28(Supplement), 1–20.

    Google Scholar 

  • Malanson, G. P., Zeng, Y., & Walsh, S. J. (2006). Complexity at advancing ecotones and frontiers. Environment and Planning A, 38(4), 619–632.

    Article  Google Scholar 

  • Manson, S. M. (2001). Simplifying complexity: A review of complexity theory. Geoforum, 32(3), 405–414.

    Article  Google Scholar 

  • Mather, A. S., Needle, C. L., & Fairbairn, J. (1998). The human drivers of global land cover change: The case of forests. Hydrological Processes, 12(13–14), 1983–1994.

    Article  Google Scholar 

  • Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: A review of applications. Landscape Ecology, 22(10), 1447–1459.

    Article  Google Scholar 

  • Mauchamp, A. (1997). Threats from alien plant species in the Galápagos Islands. Conservation Biology, 11(1), 260–263.

    Article  Google Scholar 

  • Messina, J. P., & Walsh, S. J. (2001). 2.5D morphogenesis: Modeling landuse and landcover dynamics in the Ecuadorian Amazon. Plant Ecology, 156(1), 75–88.

    Article  Google Scholar 

  • Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being: Biodiversity synthesis. Washington, DC: World Resources Institute.

    Google Scholar 

  • Minar, N., Burkhart, R., Langton, C., & Askenazi, M. (1996). The SWARM simulation system: A toolkit for building multi-agent simulations. Working Paper, The Santa Fe Institute, rep. no. 96-06-042.

  • National Research Council. (2001). Grand challenges in the environmental sciences. Washington, DC: National Academies Press.

    Google Scholar 

  • O’Sullivan, D. (2008). Geographical information science: Agent-based models. Progress in Human Geography, 32(4), 541–550.

    Article  Google Scholar 

  • Ospina, P., & Falconí, C. (Eds.). (2007). Galápagos: Migraciones, economía, cultura, conflictos y acuerdos. Ecuador: Universidad Andina Simón Bolívar.

    Google Scholar 

  • Ospina-Peralta, P. E. (2004). Galápagos, naturaleza y sociedad, actores sociales y conflictos ambientales en las islas Galápagos, Ecuador. PhD, Antropología Social. D.F., Mexico, Universidad Iberoamericana. MS: 189.

  • Papert, S. (1972). Teaching children thinking. Innovations in Education and Teaching International, 9(5), 245–255.

    Article  Google Scholar 

  • Parker, D. C. (2007). Revealing “space” in spatial externalities: Edge-effect externalities and spatial incentives. Journal of Environmental Economics and Management, 54(1), 84–99.

    Article  Google Scholar 

  • Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J., & Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers, 93(2), 314–337.

    Article  Google Scholar 

  • Portugali, J. (2006). Complexity theory as a link between space and place. Environment and Planning A, 38, 647–664.

    Article  Google Scholar 

  • Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609.

    Article  Google Scholar 

  • Ramanath, A. M., & Gilbert, N. (2004). The design of participatory agent-based social simulations. Journal of Artificial Societies and Social Simulation, 7(4). Accessed May 2009. http://jasss.soc.surrey.ac.uk/7/4/1.html.

    Google Scholar 

  • Rind, D. (1999). Complexity and climate. Science, 284(5411), 105–107.

    Google Scholar 

  • Rindfuss, R. R., Entwisle, B., Walsh, S. J., Mena, C. F., Erlien, C. M., & Gray, C. L. (2007). Frontier land use change: Synthesis, challenges, and next steps. Annals of the Association of American Geographers, 97(4), 739–754.

    Article  Google Scholar 

  • Roberts, C. A., Stallman, D., & Bieri, J. A. (2002). Modeling complex human–environment interactions: The Grand Canyon river trip simulator. Ecological Modelling, 153(1–2), 181–196.

    Article  Google Scholar 

  • Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.

    Google Scholar 

  • Soares, B. S., Cerqueira, G. C., & Pennachin, C. L. (2002). DINAMICA—A stochastic cellular automata model designed to simulate the landscape dynamics in an amazonian colonization frontier. Ecological Modelling, 154(3), 217–235.

    Article  Google Scholar 

  • Solecki, W. D., & Oliveri, C. (2004). Downscaling climate change scenarios in an urban land use change model. Journal of Environmental Management, 72(1–2), 105–115.

    Article  Google Scholar 

  • Walsh, S. J., Entwisle, B., Rindfuss, R. R., & Page, P. H. (2006). Spatial simulation modelling of land use/land cover change scenarios in northeastern Thailand: A cellular automata approach. Journal of Land Use Science, 1(1), 5–28.

    Article  Google Scholar 

  • Walsh, S. J., McCleary, A. L., Mena, C. F., Shao, Y., Tuttle, J. P., Gonzalez, A., et al. (2008a). QuickBird and hyperion data analysis of an invasive plant species in the Galápagos Islands of Ecuador: Implications for control and land use management. Remote Sensing of Environment, 112(5), 1927–1941.

    Article  Google Scholar 

  • Walsh, S. J., & McGinnis, D. (2008). Biocomplexity in coupled human-natural systems: Study of population & environment interactions. Geoforum, 39(2), 773–775.

    Article  Google Scholar 

  • Walsh, S. J., Messina, J. P., Mena, C. F., Malanson, G. P., & Page, P. H. (2008b). Complexity theory, spatial simulation models and land use dynamics in the northern Ecuadorian Amazon. Geoforum, 39, 867–878.

    Article  Google Scholar 

  • Watkins, G., & Cruz, F. (2007). Galápagos at risk: A socioeconomic analysis of the situation in the archipelago. Puerto Ayora, Province of Galápagos: Charles Darwin Foundation.

    Google Scholar 

  • West, C. T. (2009). Domestic transitions, desiccation, agricultural intensification, and Livelihood diversification among rural households on the Central Plateau, Burkina Faso. American Anthropologist, 111(3), 275–288.

    Article  Google Scholar 

  • Windrum, P., Fagiolo, G., & Moneta, A. (2007). Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies and Social Simulation, 10(2), 8.

    Google Scholar 

Download references

Acknowledgements

We are grateful to Jonathan Kropko at the Odum Institute for his guidance in model development. We would also like to thank Brian Frizzelle at the Carolina Population Center for his instruction in NetLogo functionality, Laura Brewington for her insights regarding Isabela farming livelihoods, as well as the anonymous reviewers for their comments.

Author information

Authors and Affiliations

  1. Curriculum for the Environment and Ecology, Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

    Brian W. Miller, Liza Guzmán-Ramirez & Susan C. Caplow

  2. Curriculum for the Environment and Ecology, Department of Geography, University of North Carolina, Chapel Hill, NC, USA

    Ian Breckheimer

  3. Department of Geography, Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

    Amy L. McCleary

  4. Gillings School of Global Public Health, Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

    Jessica C. Jones-Smith

  5. Department of Geography, Center for Galapagos Studies, Carolina Population Center, University of North Carolina, Chapel Hill, NC, USA

    Stephen J. Walsh

Authors
  1. Brian W. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ian Breckheimer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amy L. McCleary
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liza Guzmán-Ramirez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Susan C. Caplow
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jessica C. Jones-Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephen J. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brian W. Miller.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Miller, B.W., Breckheimer, I., McCleary, A.L. et al. Using stylized agent-based models for population–environment research: a case study from the Galápagos Islands. Popul Environ 31, 401–426 (2010). https://doi.org/10.1007/s11111-010-0110-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11111-010-0110-4

Keywords

Search

Navigation