Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success.
Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns.
We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park.
The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions.
These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.
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Models, data, and scripts used in this study are publicly available on the U.S. Geological Survey ScienceBase digital catalogue at: https://doi.org/10.5066/P9R98PPB (Miller and Frid. 2021). See Online Resource S1 for details on accessing the case study files and a sample dataset including coupled and standalone simulations and associated code files that are compatible with the latest version of SyncroSim.
NRCS ecological site descriptions are available from https://edit.jornada.nmsu.edu
NRCS soil maps and data are available from http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx
The bison range of Badlands National Park was expanded on October 11, 2019, after model development was complete, hence the use of the previous bison range boundary for this work.
For the updated model, cells in the initial state class raster that were classified as thistle or encroached by woody species were reassigned to another state class according to the proportional areas of the remaining landscape.
The updated model assumed static year-round grazing consumption, which was set to match the number of bison initialized in the ABM.
The updated biomass flows removed litter and only included decomposition, grazing, and net primary production (NPP).
Aalders I (2008) Modeling land-use decision behavior with Bayesian belief networks. Ecol Soc. https://doi.org/10.5751/ES-02362-130116
ApexRMS (2021) SyncroSim. Retrieved from https://syncrosim.com/. Accessed 26 May 2021
Baker WL (1989) A review of models of landscape change. Landsc Ecol 2(2):111–133
Balzter H, Braun PW, Kohler W (1998) Cellular automata models for vegetation dynamics. Ecol Model 107:113–125
Beeton TA, McNeeley SM, Miller BW, Ojima DS (2019) Grounding simulation models with qualitative case studies: toward a holistic framework to make climate science usable for US public land management. Clim Risk Manag 23:50–66
Belsare AV, Gompper ME (2015) A model-based approach for investigation and mitigation of disease spillover risks to wildlife: dogs, foxes and canine distemper in central India. Ecol Model 296:102–112
Bolte JP, Hulse DW, Gregory SV, Smith C (2007) Modeling biocomplexity—actors, landscapes and alternative futures. Environ Model Softw 22(5):570–579
Bonnell TR, Sengupta RR, Chapman CA, Goldberg TL (2010) An agent-based model of red colobus resources and disease dynamics implicates key resource sites as hot spots of disease transmission. Ecol Model 221(20):2491–2500
Boone RB, Galvin KA (2014) Simulation as an approach to social-ecological integration, with an emphasis on agent-based modeling. In: Manfredo MJ, Vaske JJ, Rechkemmer A, Duke EA (eds) Understanding society and natural resources. Springer, Dordrecht, pp 179–202
Bradford JB, Weltzin JF, McCormick M, Baron J, Bowen Z, Bristol S, Carlisle D, Crimmins T, Cross P, DeVivo J, Dietze M, Freeman M, Goldberg J, Hooten M, Hsu L, Jenni K, Keisman J, Kennen J, Lee K, Lesmes D, Loftin K, Miller BW, Murdoch P, Newman J, Prentice KL, Rangwala I, Read J, Sieracki J, Sofaer H, Thur S, Toevs G, Werner F, White CL, White T, Wiltermuth M (2020) Ecological forecasting—21st century science for 21st century management: 2020–1073. US Geological Survey , Virginia, p 54
Brady M, Sahrbacher C, Kellermann K, Happe K (2012) An agent-based approach to modeling impacts of agricultural policy on land use, biodiversity and ecosystem services. Landsc Ecol 27(9):1363–1381
Brown JR (2010) Ecological sites: their history, status, and future. Rangel 32:5–8
Buckland S (1984) Monte Carlo confidence intervals. Biom 40(3):811–817
Chaves-Fonnegra A, Riegl B, Zea S, Lopez JV, Smith T, Brandt M, Gilliam DS (2018) Bleaching events regulate shifts from corals to excavating sponges in algae-dominated reefs. Glob Chang Biol 24(2):773–785
Clarke KC (2014) Cellular automata and agent-based models. In: Fischer MM, Nijkamp P (eds) Handbook of regional science. Springer, Berlin, pp 1217–1233
Coppedge BR, Shaw JH (1998) Bison grazing patterns on seasonally burned tallgrass prairie. Rangel Ecol Manag/J Range Manag Arch 51(3):258–264
Coppock DL, Detling JK (1986) Alteration of bison and black-tailed prairie dog grazing interaction by prescribed burning. J Wildl Manag 50(3):452–455
Coppock DL, Ellis JE, Detling JK, Dyer MI (1983) Plant-herbivore interactions in a North American mixed-grass prairie. II. Responses of bison to modification of vegetation by prairie dogs. Oecologia 56:10–15
Costanza JK, Abt RC, McKerrow AJ, Collazo JA (2015) Linking state-and-transition simulation and timber supply models for forest biomass production scenarios. AIMS Environ Sci 2(2):180–202
Creutzburg MK, Henderson EB, Conklin DR (2015) Climate change and land management impact rangeland condition and sage-grouse habitat in southeastern Oregon. AIMS Environ Sci 2:203–236
Daniel CJ, Frid L, Sleeter BM, Fortin MJ (2016) State-and-transition simulation models: a framework for forecasting landscape change. Methods Ecol Evol 7(11):1413–1423
Daniel CJ, Sleeter BM, Frid L, Fortin MJ (2018) Integrating continuous stocks and flows into state-and-transition simulation models of landscape change. Methods Ecol Evol 9(4):1133–1143
DeAngelis DL, Diaz SG (2019) Decision-making in agent-based modeling: a current review and future prospectus. Front Ecol Evol 6:237
DeAngelis DL, Mooij WM (2005) Individual-based modeling of ecological and evolutionary processes. Annu Rev Ecol Evol Syst 36:147–168
Department of the Interior (2014) DOI bison report: looking forward. Natural resource report NPS/NRSS/BRMD/NRR–2014/821. National Park Service, Fort Collins
Dietze MC (2017) Ecological forecasting. Princeton University Press, Princeton
Feist M (2000) Basic nutrition of bison. Agriculture Knowledge Centre, Saskatchewan
Fontaine CM, Rounsevell MD (2009) An agent-based approach to model future residential pressure on a regional landscape. Landsc Ecol 24(9):1237–1254
Ford PL, Reeves MC, Frid L (2018) A tool for projecting rangeland vegetation response to management and climate. Rangelands. https://doi.org/10.1016/j.rala.2018.10.010
Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF (2010) The ODD protocol: a review and first update. Ecol Model 221(23):2760–2768
Hart SJ, Henkelman J, McLoughlin PD, Nielsen SE, Truchon-Savard A, Johnstone JF (2019) Examining forest resilience to changing fire frequency in a fire-prone region of boreal forest. Glob Chang Biol 25(3):869–884
Hess B, Dreber N, Liu Y, Wiegand K, Ludwig M, Meyer H, Meyer KM (2020) PioLaG: a piosphere landscape generator for savanna rangeland modelling. Landsc Ecol 35(9):2061–2082
Hijmans RJ (2020) Raster: geographic data analysis and modeling. R package version 3.1–5. https://CRAN.R-project.org/package=raster. Accessed 24 Sept 2021
Hovel KA, Regan HM (2008) Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator-prey relationships in seagrass landscapes. Landsc Ecol 23(1):75–89
Jarnevich CS, Cullinane Thomas C, Young NE, Backer D, Cline S, Frid L, Grissom P (2019) Developing an expert elicited simulation model to evaluate invasive species and fire management alternatives. Ecosphere 10(5):e02730
Jaxa-Rozen M, Kwakkel JH, Bloemendal M (2019) A coupled simulation architecture for agent-based/geohydrological modelling with NetLogo and MODFLOW. Env Model Softw 115:19–37
Johnstone JF, Rupp TS, Olson M, Verbyla D (2011) Modeling impacts of fire severity on successional trajectories and future fire behavior in Alaskan boreal forests. Landsc Ecol 26(4):487–500
Manson SM (2001) Simplifying complexity: a review of complexity theory. Geoforum 32(3):405–414
Manzo G, Matthews T (2014) Potentialities and limitations of agent-based simulations. Rev Fr Sociol 55(4):653–688
Matthews RB, Gilbert NG, Roach A, Polhill JG, Gotts NM (2007) Agent-based land-use models: a review of applications. Landsc Ecol 22(10):1447–1459
Miller BW, Frid L (2021) Improving projections of wildlife and landscapes for natural resource managers. US Geological Survey , Virginia
Miller BW, Morisette JT (2014) Integrating research tools to support the management of social-ecological systems under climate change. Ecol Soc. https://doi.org/10.5751/ES-06813-190341
Miller BW, Breckheimer I, McCleary AL, Guzmán-Ramirez L, Caplow SC, Jones-Smith JC, Walsh SJ (2010) Using stylized agent-based models for population–environment research: a case study from the Galápagos Islands. Popul Environ 31(6):401–426
Miller BW, Frid L, Chang T, Piekielek N, Hansen AJ, Morisette JT (2015) Combining state-and-transition simulations and species distribution models to anticipate the effects of climate change. AIMS Environ Sci 2(2):400–426
Miller BW, Symstad AJ, Frid L, Fisichelli NA, Schuurman GW (2017) Co-producing simulation models to inform resource management: a case study from southwest South Dakota. Ecosphere. https://doi.org/10.1002/ecs2.2020
Miyasaka T, Le QB, Okuro T, Zhao X, Takeuchi K (2017) Agent-based modeling of complex social–ecological feedback loops to assess multi-dimensional trade-offs in dryland ecosystem services. Landsc Ecol 32(4):707–727
Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):314–337
Portugali J (2006) Complexity theory as a link between space and place. Env Plan A 38:647–664
Provencher L, Frid L, Czembor C, Morisette JT (2016) State-and-transition models: conceptual versus simulation perspectives, usefulness and breadth of use, and land management applications. In: Germino MJ, Chambers JC, Brown CS (eds) Exotic brome-grasses in arid and semiarid ecosystems of the western US. Springer International Publishing, Cham, pp 371–407
R Core Team (2018) R: a language environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/. Accessed 24 Sept 2021
Railsback SF, Grimm V (2019) Agent-based and individual-based modeling: a practical introduction. Princeton University Press, Princeton
Rebaudo F, Crespo-Pérez V, Silvain JF, Dangles O (2011) Agent-based modeling of human-induced spread of invasive species in agricultural landscapes: insights from the potato moth in Ecuador. J Artif Soc Soc Simul 14(3):7
Rupp TS, Starfield AM, Chapin FS (2000) A frame-based spatially explicit model of subarctic vegetation response to climatic change: comparison with a point model. Landsc Ecol 15(4):383–400
Senior AM, Krkosek M, Nakagawa S (2013) The practicality of Trojan sex chromosomes as a biological control: an agent based model of two highly invasive Gambusia species. Biol Invasions 15(8):1765–1782
Sibly RM, Grimm V, Martin BT, Johnston AS, Kułakowska K, Topping CJ, Calow P, Nabe-Nielsen J, Thorbek P, DeAngelis DL (2013) Representing the acquisition and use of energy by individuals in agent-based models of animal populations. Methods Ecol Evol 4(2):151–161
Sleeter BM, Liu J, Daniel C, Rayfield B, Sherba J, Hawbaker TJ, Zhu Z, Selmants PC, Loveland TR (2018) Effects of contemporary land-use and land-cover change on the carbon balance of terrestrial ecosystems in the United States. Environ Res Lett 13(4):045006
Spies TA, White E, Ager A, Kline JD, Bolte JP, Platt EK, Olsen KA, Pabst RJ, Barros AM, Bailey JD, Charnley S (2017) Using an agent-based model to examine forest management outcomes in a fire-prone landscape in Oregon, USA. Ecol Soc. https://doi.org/10.5751/ES-08841-220125
Symstad AJ, Miller BW, Shenk TM, Athearn ND, Runge MC (2019) A draft decision framework for the National park service interior region 5 bison stewardship strategy. Natural resource report NPS/MWRO/NRR—2019/2046. National Park Service, Fort Collins
Tang W, Bennett DA (2010) Agent-based modeling of animal movement: a review. Geogr Compass 4(7):682–700
Thiele JC (2014) R marries NetLogo: introduction to the RNetLogo package. J Stat Softw 58(2):1–41
Thiele JC, Kurth W, Grimm V (2012) RNetLogo: an R package for running and exploring individual-based models implemented in NetLogo. Methods Ecol Evol 3(3):480–483
Tracey JA, Bevins SN, VandeWoude S, Crooks KR (2014) An agent-based movement model to assess the impact of landscape fragmentation on disease transmission. Ecosphere 5(9):1–24
Valbuena D, Verburg PH, Bregt AK, Ligtenberg A (2010) An agent-based approach to model land-use change at a regional scale. Landsc Ecol 25(2):185–199
Voinov A, Bousquet F (2010) Modelling with stakeholders. Env Model Softw 25:1268–1281
Wang HH, Grant WE, Elliott NC, Brewer MJ, Koralewski TE, Westbrook JK, Alves TM, Sword GA (2019) Integrated modelling of the life cycle and aeroecology of wind-borne pests in temporally-variable spatially-heterogeneous environment. Ecol Model 399:23–38
Westoby M, Walker B, Noy-Meir I (1989) Opportunistic management for rangelands not at equilibrium. J Range Manag 42:266–274
Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York
Wilensky U (1999) NetLogo. Center for connected learning and computer-based modeling, Northwestern University, Evanston. http://ccl.northwestern.edu/netlogo/. Accessed 24 Sept 2021
Wilson TS, Sleeter BM, Sherba J, Cameron D (2015) Land-use impacts on water resources and protected areas: applications of state-and-transition simulation modeling of future scenarios. AIMS Environ Sci 2(2):282–301
Yospin GI, Bridgham SD, Neilson RP, Bolte JP, Bachelet DM, Gould PJ, Harrington CA, Kertis JA, Evers C, Johnson BR (2015) A new model to simulate climate-change impacts on forest succession for local land management. Ecol Appl 25(1):226–242
We are grateful to Josie Hughes and Alex Embrey for helping develop the coupled model script, Tyler Beeton for sharing an ABM that served as the seed for the one described here, and Shreeram Senthivasan for reviewing the models and updating the models and documentation in the sample dataset. We also thank Catherine Jarnevich and three anonymous reviewers for their thoughtful comments on previous drafts of the paper. This research was funded by the U.S. Geological Survey, North Central Climate Adaptation Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
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Miller, B.W., Frid, L. A new approach for representing agent-environment feedbacks: coupled agent-based and state-and-transition simulation models. Landscape Ecol (2021). https://doi.org/10.1007/s10980-021-01282-y
- Simulation modeling