Aeroecology pp 259-275 | Cite as

Using Agent-Based Models to Scale from Individuals to Populations



All aggregate biological phenomena in the aerosphere are due to the behaviors of unique individuals acting according to their own rules of behavior and perceived external stimuli. An appealing characteristic of aeroecology is that we can observe both these aggregate behaviors of large groups (using tools such as radar and observational networks) as well as the behavior of individual animals (by employing animal tracking technology). Traditional population modeling efforts focus on equations that mimic natural populations in terms of overall population size and/or mean population parameters, often discounting mechanisms operating on the individual level that give rise to overall population dynamics. Concurrent advancements in computing capacity and animal tracking methodologies provide us with the opportunity to examine how the actions of individuals scale up to give rise to population-level phenomena in the aerosphere. More specifically, we can now model populations as a collection of individuals that behave independently, and we can validate the inferences from these agent-based models by tracking actual animals over the course of their annual cycle. In this chapter, we provide an example of how an agent-based model can be used to predict migration behavior across a species range based on a small set of actual migration tracks. The example provides a generalizable framework for using agent-based models as a link between data from individuals and broad-scale phenomena.



This reasearch was aided by support from the National Science Foundation (awards 1340921, 1152356, and 0946685) and from the United States Department of Agriculture National Institute for Food and Agriculture (award 2013-67009-20369). All authors belong to the Applied Aeroecology Group, a University Sponsored Organization at the University of Oklahoma.


  1. Botkin DB, Wallis JR, Janak JF (1972) Some ecological consequences of a computer model of forest growth. J Ecol 60:849–873CrossRefGoogle Scholar
  2. Bridge ES et al (2011) Technology on the move: recent and forthcoming innovations for tracking migratory birds. Bioscience 61:689–698CrossRefGoogle Scholar
  3. Bridge ES, Ross JD, Contina AJ, Kelly JF (2016) Do molt-migrant songbirds optimize migration routes based on primary productivity? Anim Behav 27:784–792Google Scholar
  4. Contina A, Bridge ES, Seavy NE, Duckles J, Kelly JF (2013) Using geologgers to investigate bimodal isotope patterns in painted buntings. Auk 130:265–272CrossRefGoogle Scholar
  5. Coss-Custard JD, Stillman RA (2008) Individual-based models and the management of shorebird populations. Nat Resour Model 21:3–71CrossRefGoogle Scholar
  6. DeAngelis DL, Grimm V (2014) Individual-based models in ecology after four decades. F1000Prime Rep 6:39CrossRefPubMedPubMedCentralGoogle Scholar
  7. Dennhardt AJ, Duerr AE, Brandes D, Katzner TE (2015) Modeling autumn migration of a rare soaring raptor identifies new movement corridors in central appalachia. Ecol Model 303:19–29CrossRefGoogle Scholar
  8. eBird (2012) eBird: an online database of bird distribution and abundance [web application]. eBird, Ithaca. Accessed 23 June 2015
  9. Erni B, Liechti F, Bruderer B (2003) How does a first year passerine migrant find its way? Simulating migration mechanisms and behavioural adaptations. Oikos 103:333–340CrossRefGoogle Scholar
  10. ESRI (2010) Agent analyst: agent based modeling extension for arcgis users. Environmental Systems Research Institute, RedlandsGoogle Scholar
  11. ESRI (2011) ArcGIS Desktop: Release 10. Environmental Systems Research Institute, RedlandsGoogle Scholar
  12. Fortmann-Roe S (2014) Insight maker: a general-purpose tool for web-based modeling & simulation. Simul Model Pract Theory 47:28–45CrossRefGoogle Scholar
  13. Fraser KC et al (2013) Consistent range-wide pattern in fall migration strategy of purple martin (progne subis), despite different migration routes at the Gulf of Mexico. Auk 130:291–296CrossRefGoogle Scholar
  14. Haig SM et al (2011) Genetic applications in avian conservation. Auk 128:205–229CrossRefGoogle Scholar
  15. Hallworth MT, Sillett TS, Van Wilgenburg SL, Hobson KA, Marra PP (2015) Migratory connectivity of a Neotropical migratory songbird revealed using archival light-level geolocators. Ecol Appl 25:336–347Google Scholar
  16. Hicke JA, Lobell DB, Asner GP (2004) Cropland area and net primary production computed from 30 years of usda agricultural harvest data. Earth Interact 8(10):1–20CrossRefGoogle Scholar
  17. Jahn AE et al (2013) Migration timing and wintering areas of three species of flycatchers (tyrannus) breeding in the great plains of North America. Auk 130:247–257CrossRefGoogle Scholar
  18. Kanarek AR, Lamberson RH, Black JM (2008) An individual-based model for traditional foraging behavior: investigating effects of environmental fluctuation. Nat Resour Model 21:93–116CrossRefGoogle Scholar
  19. Kaul H, Ventikos Y (2015) Investigating biocomplexity through the agent-based paradigm. Brief Bioinform 16:137–152CrossRefPubMedGoogle Scholar
  20. Kelly JF, Ruegg KC, Smith TB (2005) Combining isotopic and genetic markers to identify breeding origins of migrant birds. Ecol Appl 15:1487–1494CrossRefGoogle Scholar
  21. Kranstauber B, Weinzierl R, Wikelski M, Safi K (2015) Global aerial flyways allow efficient travelling. Ecol Lett 18:1338–1345CrossRefPubMedGoogle Scholar
  22. Leu M, Thompson CW (2002) The potential importance of migratory stopover sites as flight feather molt staging areas: a review for neotropical migrants. Biol Conserv 106:45–56CrossRefGoogle Scholar
  23. Lowther PE, Lanyon SM, Thompson CW (1999) Painted bunting (Passerina ciris). In: Poole A, Gill F (eds) The birds of North America no. 398. The Birds of North America, Philadelphia, pp 1–28Google Scholar
  24. Marceau DJ (2008) What can be learned from multi-agent systems? In: Gimblett R (ed) Monitoring, simulation and management of visitor landscapes. University of Arizona Press, Tucson, pp 411–424Google Scholar
  25. North MJ, Collier NT, Ozik J, Tatara ER, Macal CM, Bragen M, Sydelko P (2013) Complex adaptive systems modeling with Repast Simphony. Complex Adapt Syst Model 1:1–26CrossRefGoogle Scholar
  26. Parrish JK, Viscido SV, Grunbaum D (2002) Self-organized fish schools: an examination of emergent properties. Biol Bull 202:296–305CrossRefPubMedGoogle Scholar
  27. Railsback SF, Lytinen SL, Jackson SK (2006) Agent-based simulation platforms: review and development recommendations. Simul Trans Soc Model Simul Int 82:609–623Google Scholar
  28. Rakhimberdiev E, Senner NR, Verhoeven MA, Winkler DW, Bouten W, Piersma T (2016) Comparing inferences of solar geolocation data against high-precision GPS data: annual movements of a double-tagged black-tailed godwit. J Avian Biol 47(4):589–596CrossRefGoogle Scholar
  29. Reynolds CW (1987) Flocks, herds, and schools: a distributed behavior model. Comput Graph 21:25–34CrossRefGoogle Scholar
  30. Rohwer S, Butler LK, Froehlich DR (2005) Ecology and demography of east-west differences in molt scheduling in neotropical migrant passerines. In: Greenberg R, Marra PP (eds) Birds of two worlds. Johns Hopkins University Press, Baltimore, pp 87–105Google Scholar
  31. Romanowska I (2014) How the python ate the turtle. Accessed 29 June 2015Google Scholar
  32. Ruegg KC, Anderson EC, Paxton KL, Apkenas V, Lao S, Siegel RB, Desante DF, Moore F, Smith TB (2014) Mapping migration in a songbird using high-resolution genetic markers. Mol Ecol 23:5726–5739CrossRefPubMedGoogle Scholar
  33. Rushing CS, Ryder TB, Saracco JF, Marra PP (2014) Assessing migratory connectivity for a long-distance migratory bird using multiple intrinsic markers. Ecol Appl 24:445–456CrossRefPubMedGoogle Scholar
  34. Stanley CQ et al (2014) Connectivity of wood thrush breeding, wintering, and migration sites based on range-wide tracking. Conserv Biol 29:164–174CrossRefPubMedGoogle Scholar
  35. Stepanian PM (2015) Radar polarimetry for biological applications university of oklahoma. Norman, OklahomaGoogle Scholar
  36. Sumner MD, Wotherspoon SJ, Hindell MA (2009) Bayesian estimation of animal movement from archival and satellite tags. PLoS One 4:7324CrossRefGoogle Scholar
  37. Sykes PW Jr, Holzman S, Inigo-Elias EE (2007) Current range of the eastern population of painted bunting (Passerina ciris) part II: winter range. North Am Birds 61:378–406Google Scholar
  38. Tesfatsion L (2002) Agent-based computational economics: growing economies from the bottom up. Artif Life 8:55–82CrossRefPubMedGoogle Scholar
  39. Thompson CW (1991) The sequence of molts and plumages in painted buntings and implications for theories of delayed plumage maturation. Condor 93:209–235CrossRefGoogle Scholar
  40. Wang Z, Butner JD, Cristini V, Deisboeck TS (2015) Integrated PK-PD and agent-based modeling in oncology. J Pharmacokinet Pharmacodyn 42:179–189CrossRefPubMedPubMedCentralGoogle Scholar
  41. Wilensky U (1999) NetLogo. Center for connected learning and computer-based modeling, Northwestern University. Evanston.
  42. Wotherspoon S, Sumner M (2014) SGAT: Solar/satellite geolocation for animal tracking.
  43. Yorke JA, Anderson WN (1973) Predator-prey patterns. Proc Natl Acad Sci USA 70:2069–2071CrossRefPubMedPubMedCentralGoogle Scholar
  44. Zvoleff A (2014) PyABM: an open source agent-based modeling toolkit. Accessed 29 June 2015
  45. Zvoleff A, Li A (2014) Analyzing human-landscape interactions: tools that integrate. Environ Manag 53:94–111CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Oklahoma Biological SurveyUniversity of OklahomaNormanUSA
  2. 2.Oklahoma Biological Survey and Department of BiologyUniversity of OklahomaNormanUSA

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