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Next-Generation Individual-Based Models Integrate Biodiversity and Ecosystems: Yes We Can, and Yes We Must

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Abstract

Ecosystem and community ecology have evolved along different pathways, with little overlap. However, to meet societal demands for predicting changes in ecosystem services, the functional and structural view dominating these two branches of ecology, respectively, must be integrated. Biodiversity–ecosystem function research has addressed this integration for two decades, but full integration that makes predictions relevant to practical problems is still lacking. We argue that full integration requires going, in both branches, deeper by taking into account individual organisms and the evolutionary and physico-chemical principles that drive their behavior. Individual-based models are a major tool for this integration. They have matured by using individual-level mechanism to replace the demographic thinking which dominates classical theoretical ecology. Existing individual-based ecosystem models already have proven useful both for theory and application. Still, next-generation individual-based models will increasingly use standardized and re-usable submodels to represent behaviors and mechanisms such as growth, uptake of nutrients, foraging, and home range behavior. The strategy of pattern-oriented modeling then helps make such ecosystem models structurally realistic by developing theory for individual behaviors just detailed enough to reproduce and explain patterns observed at the system level. Next-generation ecosystem scientists should include the individual-based approach in their toolkit and focus on addressing real systems because theory development and solving applied problems go hand-in-hand in individual-based ecology.

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References

  • Amano T, Ushiyama K, Moriguchi S, Fujita G, Higuchi H. 2006. Decision-making in group foragers with incomplete information: test of individual-based model in geese. Ecol Monogr 76:601–16.

    Article  Google Scholar 

  • Ayllón D, Railsback SF, Vincenzi S, Groeneveld J, Almodóvar A, Grimm V. 2016. InSTREAM-Gen: modelling eco-evolutionary dynamics of trout populations under anthropogenic environmental change. Ecol Model 326:36–53.

    Article  Google Scholar 

  • Botkin DB, Janak JF, Wallis JR. 1972. Some ecological consequences of a computer model of forest growth. J Ecol 60:849–72.

    Article  Google Scholar 

  • Booth G. 1997. Gecko: a continuous 2-D world for ecological modeling. Artif Life J 3:147–63.

    Article  CAS  Google Scholar 

  • Bugmann H. 2001. A review of forest gap models. Clim Change 51:259–305.

    Article  Google Scholar 

  • Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S. 2012. Biodiversity loss and its impact on humanity. Nature 486:59–67.

    Article  CAS  PubMed  Google Scholar 

  • Cury PM, Shin YJ, Planque B, Durant JM, Fromentin JM, Kramer-Schadt S, Stenseth NC, Travers M, Grimm V. 2008. Ecosystem oceanography for global change in fisheries. Trends Ecol Evol 23:338–46.

    Article  PubMed  Google Scholar 

  • DeAngelis DL, Grimm V. 2014. Individual-based models after four decades. F1000Prime Reports 6(39):6.

    Google Scholar 

  • Evans MR, Bithell M, Cornell S, Dall SRX, Diaz S, Emmott S, Ernande B, Grimm V, Hodgson DJ, Lewis SL, Mace GM, Morecroft M, Moustakas A, Murphy E, Newbold T, Petchey O, Smith M, Travis JMJ, Benton TG. 2013a. Predictive systems ecology. Proc R Soc B 280:20131452.

    Article  PubMed  PubMed Central  Google Scholar 

  • Evans MR, Grimm V, Johst K, Knuuttila T, de Langhe R, Lessells CM, Merz M, O’Malley MA, Orzack SH, Weisberg M, Wilkinson DJ, Wolkenhauer O, Benton TG. 2013b. Do simple models lead to generality in ecology? Trends Ecol Evol 28:578–83.

    Article  PubMed  Google Scholar 

  • Fischer R, Bohn F, Dantas de Paula M, Dislich C, Groeneveld J, Gutiérrez AG, Kazmierczak M, Knapp N, Lehmann S, Paulick S, Pütz S, Roedig E, Taubert F, Köhler P, Huth A. 2016. Lessons learned from applying a gap model to complex forests and their carbon dynamics. Ecol Model 326:124–33.

    Article  CAS  Google Scholar 

  • Giacomini HC, De Marco P, Petrere M. 2009. Exploring community assembly through an individual-based model for trophic interactions. Ecol Model 220:23–39.

    Article  Google Scholar 

  • Giacomini HC, DeAngelis DL, Trexler JC, Petrere M. 2013. Trait contributions to fish community assembly emerge from trophic interactions in an individual-based model. Ecol Model 251:32–43.

    Article  Google Scholar 

  • Grimm V, Frank K, Jeltsch F, Brandl R, Uchmanski J, Wissel C. 1996. Pattern-oriented modelling in population ecology. Sci Total Environ 183:151–66.

    Article  CAS  Google Scholar 

  • Grimm V. 1999. Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol Model 115:129–48.

    Article  Google Scholar 

  • Grimm V, Railsback SF. 2005. Individual-based modeling and ecology. Princeton: Princeton University Press.

    Book  Google Scholar 

  • Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke HH, Weiner J, Wiegand T, DeAngelis DL. 2005. Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310:987–91.

    Article  PubMed  Google Scholar 

  • Grimm V, Railsback SF. 2012. Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology. Philos Trans R Soc B 367:298–310.

    Article  Google Scholar 

  • Grimm V, Berger U. 2016. Structural realism, emergence, and predictions in next-generation ecological modelling: synthesis from a special issue. Ecol Model 326:177–87.

    Article  Google Scholar 

  • Harfoot MBJ, Newbold T, Tittensor DP, Emmott S, Hutton J, Lyutsarev V, Smith MJ, Scharlemann JP, Purves DW. 2014. Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model. PLoS Biol 12:e1001841.

    Article  PubMed  PubMed Central  Google Scholar 

  • Huston M, DeAngelis D, Post W. 1988. New computer models unify ecological theory. Bioscience 38:682–91.

    Article  Google Scholar 

  • Kaiser C, Franklin O, Dieckmann U, Richter A. 2014. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol Lett 17:680–90.

    Article  PubMed  PubMed Central  Google Scholar 

  • Köhler P, Huth A. 1998. The effects of tree species grouping in tropical rainforest modelling: simulations with the individual-based model FORMIND. Ecol Model 109:301–21.

    Article  Google Scholar 

  • Kooijman SALM. 2010. dynamic energy budget theory for metabolic organisation. Cambridge: Cambridge University Press.

    Google Scholar 

  • Lin Y, Berger U, Grimm V, Ji Q-R. 2012. Differences between symmetric and asymmetric facilitation matter: exploring the interplay between modes of positive and negative plant interactions. J Ecol 100:1482–91.

    Article  Google Scholar 

  • Loreau M. 2010. Linking biodiversity and ecosystems: towards a unifying ecological theory. Philos Trans R Soc B 365:49–60.

    Article  Google Scholar 

  • Martin BT, Zimmer EI, Grimm V, Jager T. 2012. Dynamic energy budget theory meets individual-based modelling: a generic and accessible implementation. Methods Ecol Evol 3:445–9.

    Article  Google Scholar 

  • Martin BT, Jager T, Nisbet RM, Preuss TG, Grimm V. 2013. Predicting population dynamics from the properties of individuals: a cross-level test of dynamic energy budget theory. Am Nat 181:506–19.

    Article  PubMed  Google Scholar 

  • May F, Grimm V, Jeltsch F. 2009. Reversed effects of grazing on plant diversity: the role of below-ground competition and size symmetry. Oikos 118:1830–43.

    Article  Google Scholar 

  • Mokany K, Ferrier S, Connolly SR, Dunstan PK, Fulton EA, Harfoot MB, Harwood TD, Richardson AJ, Roxburgh SH, Scharlemann JPW, Tittensor DP, Westcott DA, Wintle BA. 2016. Integrating modelling of biodiversity composition and ecosystem function. Oikos 125:10–19.

    Article  Google Scholar 

  • Moya-Laraño J, Bilbao-Castro JR, Barrionuevo G, Ruiz-Lupión D, Casado LG, Montserrat M, Melián CJ, Magalhães S. 2014. Eco-evolutionary spatial dynamics: rapid evolution and isolation explain food web persistence. In: Moya-Laraño J, Rowntree J, Woodward G, Eds. Advances in ecological research, Vol. 50. Oxford: Academic Press. p 75–143.

    Google Scholar 

  • Parrott L, Kok R. 2002. A generic, individual-based approach to modelling higher trophic levels in simulation for terrestrial ecosystems. Ecol Model 154:151–78.

    Article  Google Scholar 

  • Pastor J, Naiman RJ. 1992. Selective foraging and ecosystem processes in boreal forests. Am Nat 139:690–705.

    Article  Google Scholar 

  • Pastor J, Dewey B, Moen R, Mladenoff DJ, White M, Cohen Y. 1998. Spatial patterns in the moose–forest–soil ecosystem on Isle Royale, Michigan, USA. Ecol Appl 8:411–24.

    Google Scholar 

  • Peacor SD, Allesina S, Riolo RL, Hunter TS. 2007. A new computational system, DOVE (Digital Organisms in a Virtual Ecosystem), to study phenotypic plasticity and its effects in food webs. Ecol Model 205:13–28.

    Article  Google Scholar 

  • Platt JR. 1964. Strong inference. Science 146:347–53.

    Article  CAS  PubMed  Google Scholar 

  • Piou P, Berger U, Hildenbrandt H, Grimm V, Diele K, D’Lima C. 2007. Simulating cryptic movements of a mangrove crab: recovery phenomena after small scale fishery. Ecol Model 205:110–22.

    Article  Google Scholar 

  • Railsback S, Harvey B. 2002. Analysis of habitat selection rules using an individual-based model. Ecology 83:1817–30.

    Google Scholar 

  • Railsback SF, Johnson MD. 2011. Pattern-oriented modeling of bird foraging and pest control in coffee farms. Ecol Model 222:3305–19.

    Article  Google Scholar 

  • Railsback SF, Johnson MD. 2014. Effects of land use on bird populations and pest control services on coffee farms. Proc Natl Acad Sci 111:6109–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Railsback SF, Harvey BC. 2013. Trait-mediated trophic interactions: is foraging theory keeping up? Trends Ecol Evol 28:119–25.

    Article  PubMed  Google Scholar 

  • Rose KA, Allen JI, Artioli Y, Barange M, Blackford J, Carlotti F, Cropp R, Daewel U, Edwards K, Flynn K, Hill S, Hille Ris Lambers R, Huse G, Mackinson S, Megrey BA, Moll A, Rivkin R, Salihoglu B, Schrum C, Shannon L, Shin Y, Smith SL, Smith C, Solidoro C, St John M, Zhou M. 2010. End-to-end models for the analysis of marine ecosystems: challenges, issues, and next steps. Mar Coast Fish 2:115–30.

    Article  Google Scholar 

  • Scheiter S, Langan L, Higgins SI. 2013. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol 198:957–69.

    Article  PubMed  Google Scholar 

  • Schulze ED, Mooney HA, Eds. 1993. Biodiversity and ecosystem function Berlin. Berlin: Springer.

    Google Scholar 

  • Seidl R, Rammer W, Scheller RM, Spies TA. 2012. An individual-based process model to simulate landscape-scale forest ecosystem dynamics. Ecol Model 231:87–100.

    Article  Google Scholar 

  • Shin YJ, Cury P. 2001. Exploring fish community dynamics through size-dependent trophic interactions using a spatialized individual-based model. Aquat Living Res 14:65–80.

    Article  Google Scholar 

  • Shugart HH. 1984. A theory of forest dynamics: the ecological implications of forest succession models. New York: Springer.

    Book  Google Scholar 

  • Sibly RM, Grimm V, Martin BT, Johnston ASA, 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 4:151–61.

    Article  Google Scholar 

  • Smith B, Wårlind D, Arneth A, Hickler T, Leadley P, Siltberg J, Zaehle S. 2014. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11:2027–54.

    Article  Google Scholar 

  • Stillman RA, Goss-Custard JD. 2010. Individual-based ecology of coastal birds. Biol Rev 85:413–34.

    Article  PubMed  Google Scholar 

  • Stillman RA, Railsback SF, Giske J, Berger U, Grimm V. 2015. Making predictions in a changing world: the benefits of individual-based ecology. Bioscience 65:140–50.

    Article  PubMed  Google Scholar 

  • Tilman D. 1999. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80:1455–74.

    Google Scholar 

  • Travers-Trolet M, Shin YJ, Field JG. 2014. An end-to-end coupled model ROMS-N2P2Z2D2-OSMOSE of the southern Benguela foodweb: parameterisation, calibration and pattern-oriented validation. Afr J Mar Sci 36:11–29.

    Article  Google Scholar 

  • van der Vaart E, Johnston AS, Sibly RM. 2016. Predicting how many animals will be where: how to build, calibrate and evaluate individual-based models. Ecol Model 326:113–23.

    Article  Google Scholar 

  • Weiner J, Stoll P, Müller-Landau H, Jasentuliyana A. 2001. The effects of density, spatial pattern, and competitive symmetry on size variation in simulated plant populations. Am Nat 158:438–50.

    Article  CAS  PubMed  Google Scholar 

  • Wiegand T, Jeltsch F, Hanski I, Grimm V. 2003. Using pattern-oriented modeling for revealing hidden information: a key for reconciling ecological theory and conservation practice. Oikos 100:209–22.

    Article  Google Scholar 

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Acknowledgements

We thank Monica Turner and Steve Carpenter for their invitation to contribute to this special feature of Ecosystems and for their comments on an earlier draft.

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Correspondence to Volker Grimm.

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VG, SR, and DA wrote the paper; DA did the literature review of Table 1.

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Grimm, V., Ayllón, D. & Railsback, S.F. Next-Generation Individual-Based Models Integrate Biodiversity and Ecosystems: Yes We Can, and Yes We Must. Ecosystems 20, 229–236 (2017). https://doi.org/10.1007/s10021-016-0071-2

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