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Cited by 93Greenhouse Technology and Climate ControlEcology and Vegetation Dynamics StudiesForest ecology and management

An overview of agent-based models in plant biology and ecology

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Zhang, Bo, DeAngelis, Donald L·2020·Annals of Botany
DOI 10.1093/aob/mcaa043PMC PMC7489105
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Abstract

Agent-based modelling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modelling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modelling, by functional–structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition and reproduction across the scales from the plant organ to populations and communities on a range of spatial scales to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.

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  • ABM has become a well-established methodology in biology, spanning various scales from molecular mechanisms to ecological populations and communities.

    “ABM has become a well-established methodology in biology, spanning various scales from molecular mechanisms to ecological populations and communities (Zhang and DeAngelis, 2020).”

    From Towards integrated management of sandy anthropogenic shores: Developing tools for the exploration of human-natural interactions (2025) · cites this paper as Zhang and DeAngelis, 2020

    The Abstract says ABM is an established methodology in many areas of biology, ranging from the cellular to ecological population and community levels. That supports the broad claim, but the cited range begins at the cellular level; our copy does not establish its use for molecular mechanisms. (LLM, conf=0.88)

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Research works printed but not linked: 80

  1. Aber JD. 1982. FORTNITE: a computer model of organic matter and ni- trogen dynamics in forest ecosystem. Wisconsin: University College of Agricultural and Life Sciences. Research Division. Research bulletin (USA).
  2. Adams TP, Purves DW, Pacala SW. 2007. Understanding height-structured competition in forests: is there an R* for light? Proceedings of the Royal Society B: Biological Sciences 274: 3039-3047.
  3. Adler PB, Ellner SP, Levine JM. 2010. Coexistence of perennial plants: an embarrassment of niches. Ecology Letters 13: 1019-1029.
  4. Alftine KJ, Malanson GP. 2004. Directional positive feedback and pattern at an alpine tree line. Journal of Vegetation Science 15: 3-12.
  5. Allison SD. 2012. A trait-based approach for modelling microbial litter decom- position. Ecology Letters 15: 1058-1070.
  6. Barillot R, Chambon C, Fournier C, Combes D, Pradal C, Andrieu B. 2019. Investigation of complex canopies with a functional-structural plant model as exemplified by leaf inclination effect on the functioning of pure and mixed stands of wheat during grain filling. Annals of Botany 123: 727-742.
  7. Bellard C, Cassey P, Blackburn TM. 2016. Alien species as a driver of recent extinctions. Biology Letters 12: 20150623.
  8. Berger U, Hildenbrandt H, Grimm V. 2004. Age-related decline in forest production: modelling the effects of growth limitation, neighbourhood competition and self-thinning. Journal of Ecology 92: 846-853.
  9. Berger U, Rivera-Monroy VH, Doyle TW, et al. 2008. Advances and limita- tions of individual-based models to analyze and predict dynamics of man- grove forests: a review. Aquatic Botany 89: 260-274.
  10. Botkin DB. 1993. Forest dynamics: an ecological model. Oxford: Oxford University Press.
  11. Botkin DB, Wallis JR, Janak JF. 1972. Some ecological consequences of a computer model of forest growth. Journal of Ecology 60: 849-&.
  12. Bridle JR, Polechová J, Kawata M, Butlin RK. 2010. Why is adaptation pre- vented at ecological margins? New insights from individual-based simula- tions. Ecology Letters 13: 485-494.
  13. Brown C, Illian JB, Burslem DFRP. 2016. Success of spatial statistics in determining underlying process in simulated plant communities. Journal of Ecology 104: 160-172.
  14. Bugmann H. 2001. A review of forest gap models. Climatic Change 51: 259-305.
  15. Bugmann H, Grote R, Lasch P, Lindner M, Suckow F. 1997. A new forest gap model to study the effects of environmental change on forest structure and functioning. In: Mohren GMJ, Koen Kramer, Sabaté S, eds. Impacts of global change on tree physiology and forest ecosystems. Dordrecht: Springer, 255-261.
  16. Burchill CA, Kenkel NC. 1991. Vegetation environment relationships of an inland boreal salt pan. Canadian Journal of Botany 69: 722-732.
  17. Callaway RM. 1995. Positive interactions among plants. Botanical Review 61: 306-349.
  18. Canham CD, Finzi AC, Pacala SW, Burbank DH. 1994. Causes and con- sequences of resource heterogeneity in forests: interspecific variation in light transmission by canopy trees. Canadian Journal of Forest Research 24: 337-349.
  19. Canham CD, LePage PT, Coates KD. 2004. A neighborhood analysis of canopy tree competition: effects of shading versus crowding. Canadian Journal of Forest Research 34: 778-787.
  20. Carlucci MB, Streit H, Duarte LDS, Pillar VD. 2012. Individual-based trait analyses reveal assembly patterns in tree sapling communities. Journal of Vegetation Science 23: 176-186.
  21. Chen R, Twilley RR. 1998. A gap dynamic model of mangrove forest devel- opment along gradients of soil salinity and nutrient resources. Journal of Ecology 86: 37-51.
  22. Cieslak M, Seleznyova AN, Prusinkiewicz P, Hanan J. 2011. Towards aspect-oriented functional-structural plant modelling. Annals of Botany 108: 1025-1041.
  23. Czárán T. 1998. Spatiotemporal models of population and community dy- namics. Cham: Springer Science & Business Media.
  24. Damgaard C, Weiner J. 2008. Modeling the growth of individuals in crowded plant populations. Journal of Plant Ecology 1: 111-116.
  25. DeJong TM, Da Silva D, Vos J, Escobar-Gutiérrez AJ. 2011. Using func- tional-structural plant models to study, understand and integrate plant de- velopment and ecophysiology. Annals of Botany 108: 987-989.
  26. Draye X, Kim Y, Lobet G, Javaux M. 2010. Model-assisted integration of physiological and environmental constraints affecting the dynamic and spatial patterns of root water uptake from soils. Journal of Experimental Botany 61: 2145-2155.
  27. Evers JB, van der Werf W, Stomph TJ, Bastiaans L, Anten NPR. 2019. Understanding and optimizing species mixtures using functional-structural plant modelling. Journal of Experimental Botany 70: 2381-2388.
  28. Evers JB, Vos J, Yin X, Romero P, van der Putten PE, Struik PC. 2010. Simulation of wheat growth and development based on organ-level photo- synthesis and assimilate allocation. Journal of Experimental Botany 61: 2203-2216.
  29. Faverjon L, Escobar-Gutiérrez A, Litrico I, Julier B, Louarn G. 2019. A generic individual-based model can predict yield, nitrogen content, and species abundance in experimental grassland communities. Journal of Experimental Botany 70: 2491-2504.
  30. Finkelshtein D, Kondratiev Y, Kutoviy O. 2009. Individual-based model with competition in spatial ecology. Siam Journal on Mathematical Analysis 41: 297-317.
  31. Ford E, Diggle P. 1981. Competition for light in a plant monoculture modelled as a spatial stochastic process. Annals of Botany 48: 481-500.
  32. Garin G, Fournier C, Andrieu B, Houlès V, Robert C, Pradal C. 2014. A modelling framework to simulate foliar fungal epidemics using func- tional-structural plant models. Annals of Botany 114: 795-812.
  33. Gaudio N, Escobar-Gutierrez AJ, Casadebaig P, et al. 2019. Current know- ledge and future research opportunities for modeling annual crop mix- tures. A review. Agronomy for Sustainable Development 39: 20.
  34. Gayler S, Grams TE, Heller W, Treutter D, Priesack E. 2008. A dynamical model of environmental effects on allocation to carbon-based secondary compounds in juvenile trees. Annals of Botany 101: 1089-1098.
  35. Gayler S, Priesack E. 2006. PLATHO-a dynamic plant growth model consid- ering competition between individuals and allocation to carbon-based sec- ondary compounds. In: 2006 Second International Symposium on Plant Growth Modeling and Applications. Piscataway, NJ: IEEE, 85-92.
  36. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. 2010. The ODD protocol: a review and first update. Ecological modelling 221: 2760-2768.
  37. Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8: 993-1009.
  38. Han L, Gresshoff PM, Hanan J. 2011. A functional-structural modelling ap- proach to autoregulation of nodulation. Annals of Botany 107: 855-863.
  39. Kleyer M, Bekker R, Knevel I, et al. 2008. The LEDA Traitbase: a database of life-history traits of the Northwest European flora. Journal of Ecology 96: 1266-1274.
  40. Krug R, Roura-Pascual N, Richardson D. 2009. Prioritising areas for the management of invasive alien plants in the CFR: different strategies, dif- ferent priorities? South African Journal of Botany 2: 408-409.
  41. Lacointe A. 2000. Carbon allocation among tree organs: a review of basic pro- cesses and representation in functional-structural tree models. Annals of Forest Science 57: 521-533.
  42. Le Roux X, Lacointe A, Escobar-Gutiérrez A, Le Dizès S. 2001. Carbon- based models of individual tree growth: a critical appraisal. Annals of Forest Science 58: 469-506.
  43. Lin Y, Berger U, Grimm V, Ji QR. 2012. Differences between symmetric and asymmetric facilitation matter: exploring the interplay between modes of positive and negative plant interactions. Journal of Ecology 100: 1482-1491.
  44. Lindenmayer A. 1968. Mathematical models for cellular interactions in de- velopment. I. Filaments with one-sided inputs. Journal of Theoretical Biology 18: 280-299.
  45. Lindenmayer A, Prusinkiewicz P. 1990. The algorithmic beauty of plants: New York: Springer.
  46. Louarn G, Barillot R, Combes D, Escobar-Gutiérrez A. 2020. Towards intercrop ideotypes: non-random trait assembly can promote overyielding and stability of species proportion in simulated legume-based mixtures. Annals of Botany 126: 671-685.
  47. Louarn G, Faverjon L. 2018. A generic individual-based model to simulate morphogenesis, C-N acquisition and population dynamics in contrasting forage legumes. Annals of Botany 121: 875-896.
  48. Manson SM, Evans T. 2007. Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States. Proceedings of the National Academy of Sciences of the United States of America 104: 20678-20683.
  49. Mathieu A, Cournède PH, Letort V, Barthélémy D, de Reffye P. 2009. A dynamic model of plant growth with interactions between development and functional mechanisms to study plant structural plasticity related to trophic competition. Annals of Botany 103: 1173-1186.
  50. Pacala SW, Canham CD, Silander Jr J. 1993. Forest models defined by field measurements: I. The design of a northeastern forest simulator. Canadian Journal of Forest Research 23: 1980-1988.
  51. Pfestorf H, Korner K, Sonnemann I, Wurst S, Jeltsch F. 2016. Coupling experimental data with individual-based modelling reveals differential ef- fects of root herbivory on grassland plant co-existence along a resource gradient. Journal of Vegetation Science 27: 269-282.
  52. Pickett ST. 1989. Space-for-time substitution as an alternative to long-term studies. In: Likens GE, ed. Long-term studies in ecology. Berlin: Springer, 110-135.
  53. Porté A, Bartelink H. 2002. Modelling mixed forest growth: a review of models for forest management. Ecological Modelling 150: 141-188.
  54. Pretzsch H, Grote R, Reineking B, Rötzer T, Seifert S. 2008. Models for forest ecosystem management: a European perspective. Annals of Botany 101: 1065-1087.
  55. Prusinkiewicz P. 1998. Modeling of spatial structure and development of plants: a review. Scientia Horticulturae 74: 113-149.
  56. Purves DW, Lichstein JW, Strigul N, Pacala SW. 2008. Predicting and under- standing forest dynamics using a simple tractable model. Proceedings of the National Academy of Sciences of the United States of America 105: 17018-17022.
  57. Reyes F, Pallas B, Pradal C, et al. 2020. MuSCA: a multi-scale source-sink carbon allocation model to explore carbon allocation in plants. An applica- tion to static apple tree structures. Annals of Botany 126: 571-585.
  58. Risk N, Wagner-Riddle C, Furon A, Warland J, Blodau C. 2014. Comparison of simultaneous soil profile N 2 O concentration and surface N 2 O flux measurements overwinter and at spring thaw in an agricultural soil. Soil Science Society of America Journal 78: 180-193.
  59. Sarlikioti V, de Visser PH, Marcelis LF. 2011. Exploring the spatial distri- bution of light interception and photosynthesis of canopies by means of a functional-structural plant model. Annals of Botany 107: 875-883.
  60. Shinozaki K, Yoda K, Hozumi K, Kira T. 1964. A quantitative analysis of plant form-the pipe model theory: I. Basic analyses. Japanese Journal of Ecology 14: 97-105.
  61. Shugart H, Smith T. 1996. A review of forest patch models and their applica- tion to global change research. Climatic Change 34: 131-153.
  62. Shugart HH, Smith TM, Post WM. 1992. The potential for application of individual-based simulation-models for assessing the effects of global change. Annual Review of Ecology and Systematics 23: 15-38.
  63. Shugart HH, Wang B, Fischer R, et al. 2018. Gap models and their individual- based relatives in the assessment of the consequences of global change. Environmental Research Letters 13: 033001.
  64. Shugart HH, West DC, Emanuel W. 1981. Patterns and dynamics of forests: an application of simulation models. In: West DC, Shugart HH, Botkin DB, eds. Forest succession. Berlin: Springer, 74-94.
  65. Shugart HH, West DC. 1977. Development of an Appalachian deciduous forest succession model and its application to assessment of the im- pact of the chestnut blight. Journal of Environmental Management 5: 161-179.
  66. Steel J, Weiss J, Morfe T. 2014. To weed or not to weed?: The application of an agent-based model to determine the costs and benefits of different man- agement strategies. Plant Protection Quarterly 29: 101.
  67. Thornley JH, Johnson IR. 1990. Plant and crop modeling. Oxford: Clarendon Press.
  68. Thornley JHM. 1995. Shoot-root allocation with respect to C, N and P -an investigation and comparison of resistance and teleonomic models. Annals of Botany 75: 391-405.
  69. Urban DL, Acevedo MF, Garman SL. 1999. Scaling fine-scale processes to large-scale patterns using models derived from models: meta-models. In: Mladenoff DJ, Baker WL, eds. Spatial modeling of forest landscape change: approaches and applications. Cambridge: Cambridge University Press, 70-98.
  70. Vandermeer JH. 1992. The ecology of intercropping. Cambridge: Cambridge University Press.
  71. Weinstein DA, Yanai RD. 1994. Integrating the effects of simultaneous mul- tiple stresses on plants using the simulation model TREGRO. Journal of Environmental Quality 23: 418-428.
  72. White J. 1979. The plant as a metapopulation. Annual Review of Ecology and Systematics 10: 109-145.
  73. Wiegand T, Camarero JJ, Ruger N, Gutierrez E. 2006. Abrupt population changes in treeline ecotones along smooth gradients. Journal of Ecology 94: 880-892.
  74. Wilson JB. 1988. A review of evidence on the control of shoot-root ratio, in relation to models. Annals of Botany 61: 433-449.
  75. Wyszomirski T. 1983. A simulation-model of the growth of competing indi- viduals of a plant-population. Ekologia Polska-Polish Journal of Ecology 31: 73-92.
  76. Xi W, Coulson RN, Birt AG, et al. 2009. Review of forest landscape models: types, methods, development and applications. Acta Ecologica Sinica 29: 69-78.
  77. Xu L, Henke M, Zhu J, Kurth W, Buck-Sorlin G. 2011. A functional- structural model of rice linking quantitative genetic information with mor- phological development and physiological processes. Annals of Botany 107: 817-828.
  78. Zakharova L, Meyer K, Seifan M. 2019. Trait-based modelling in ecology: a review of two decades of research. Ecological Modelling 407: 108703.
  79. Zhang WX, Miller PA, Smith B, Wania R, Koenigk T, Doscher R. 2013. Tundra shrubification and tree-line advance amplify arctic climate warming: results from an individual-based dynamic vegetation model. Environmental Research Letters 8: 034023.
  80. Zhang X, Zhang B, Feeley KJ, Wang GG, Zhang J, Zhai L. 2019. Ecological contingency in species shifts: downslope shifts of woody species under warming climate and land-use change. Environmental Research Letters 14: 114033.

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Authors
Zhang, B., & DeAngelis, D. L.
Affiliations
  • University of California, Davis (US)
  • U.S. Geological Survey, Wetland and Aquatic Research Center (US)
Corresponding author
DeAngelis, Donald L
ORCID
Venue
Annals of Botany
Publisher
Oxford University Press (OUP)
Published
2020-03-16
Volume
126
Issue
4
Pages
539-557
ISSN
  • 0305-7364 (ISSN-L)
  • 1095-8290
DOI
10.1093/aob/mcaa043
PMC
PMC7489105
OpenAlex
W3011590841
Cited by
93
References
2
Citations by year
2026: 24 · 2025: 19 · 2024: 17 · 2023: 10 · 2022: 8 · 2021: 11 · 2020: 4
FWCI
0.70
Type
journal article
Language
en
Open access
Locations
  • Annals of Botany (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Geological Survey’s Greater Everglades Priority Ecosystem Science Program (DD (C) GEPES ATLSS T5.17)
  • UC Davis Chancellors’ postdoc fellowship
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Greenhouse Technology and Climate Control
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Forest ecology and management
Keywords
agent-based modeling, plant ecology, functional-structural plant model, plant architecture, plant growth, plant mortality, carbon allocation, plant competition, plant reproduction, spatial scale, landscape ecology, multiscale modeling
MeSH terms
Carbon, Ecology, Plants

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