Skip to main content
Log in

Dimensional approaches to designing better experimental ecosystems: a practitioners guide with examples

  • Special topic: Scaling-up in ecology
  • Published:
Oecologia Aims and scope Submit manuscript

Abstract

Enclosed, experimental ecosystems (“mesocosms”) are now widely used research tools in ecology. However, the small size, short duration and often simplified biological and physical complexity of mesocosm experiments raises questions about extrapolating results from these miniaturized ecosystems to nature. Dimensional analysis, a technique widely used in engineering to create scale models, employs “compensatory distortion” as a means of maintaining functional similarity in properties and relationships of interest. An earlier paper outlined a general approach to applying dimensional analysis to the construction and interpretation of mesocosm experiments (Petersen and Hastings in Am Nat 157:324, 2001). In this paper we use examples, largely drawn from the aquatic literature, to illustrate how dimensional approaches might be used to maintain key ecological properties. Such key properties include effective habitat size, environmental variability, vertical and horizontal gradients, and interactions among habitats. We distinguish both continuous and discrete approaches that can be used to achieve functional similarity through compensatory distortion. In addition to its potential as a tool for improving the realism of experimental ecosystems, the dimensional approach points towards new options for developing, testing and advancing our understanding of scaling relationships in nature.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Adey WH, Loveland K (1991) Dynamic aquaria: building living ecosystems. Academic, San Diego

    Google Scholar 

  • Bergström U, Englund G (2002) Estimating predation rates in experimental systems: scale- dependent effects of aggregative behavior. Oikos 97:251–259

    Article  Google Scholar 

  • Bergström U, Englund G (2004) Spatial scale, heterogeneity and functional response. J Anim Ecol 73:487–493

    Article  Google Scholar 

  • Carpenter SR (1999) Microcosm experiments have limited relevance for community and ecosystem ecology: reply. Ecology 80:1085–1088

    Google Scholar 

  • Cohen JE, Tilman D (1996) Biosphere 2 and biodiversity: the lessons so far. Science 274:1150–1151

    Article  PubMed  CAS  Google Scholar 

  • Cooke GD (1967) The pattern of autotrophic succession in laboratory microcosms. Bioscience 17: 717–721

    Article  Google Scholar 

  • Doering PH, Oviatt CA, Nowicki BL, Klos EG, Reed LW (1995) Phosphorus and nitrogen limitation of primary production in a simulated estuarine gradient. Mar Ecol-Prog Ser 124:271–287

    Article  CAS  Google Scholar 

  • Duarte CM, Maso M, Merino M (1992) The relationship between mesoscale phytoplankton heterogeneity and hydrographic variability. Deep-Sea Res 39:45–54

    Article  Google Scholar 

  • Englund G, Cooper SD (2003) Scale effects and extrapolation in ecological experiments. Adv Ecol Res 92:161–213

    Article  Google Scholar 

  • Enriquez S, Duarte CM, Sand-Jensen K, Nielsen SL (1996) Broad-scale comparison of photosynthetic rates across phototrophic organisms. Oecologia 108:197–206

    Google Scholar 

  • Estrada M, Alcaraz M, Marrase C (1987) Effect of reversed light gradients on the phytoplankton composition in marine microcosms. Inv Pesq 51:443–458

    Google Scholar 

  • Gervais F, Hintze T, Behrendt H (1999) An incubator for the simulation of a fluctuating light climate in studies of planktonic primary productivity. Int Rev Hydrobiol 84:49–60

    Google Scholar 

  • Gieskes W, GW K, MA B (1979) Current 14C methods for measuring primary production: gross underestimates in oceanic water. Neth J Sea Res 13:58–78

    Article  CAS  Google Scholar 

  • Gilbert F, Gonzalez A, Evans-Freke I (1998) Corridors maintain species richness in the fragmented landscapes of a microecosystem. P Roy Soc Lond B Bio 265:577–582

    Article  Google Scholar 

  • Hastings A (1990) Spatial heterogeneity and ecological models. Ecology 71:426–42

    Article  Google Scholar 

  • Have A (1990) Microslides as microcosms for the study of ciliate communities. T Am Microsc Soc 109:129–140

    Article  Google Scholar 

  • Helms SE, Hunter MD (2005) Variation in plant quality and the population dynamics of herbivores: there is nothing average about aphids. Oecologia DOI 10.1007/s00442-005-0060-1

  • Hines AH, Whitlatch RB, Thrush SF, Hewitt JE, Cummings VJ, Dayton PK, Legendre P (1997) Nonlinear foraging response of a large marine predator to benthic prey: eagle ray pits and bivalves in a New Zealand sandflat. J Exp Mar Biol Ecol 216:191–210

    Article  Google Scholar 

  • Huffaker CB (1958) Experimental studies on predation: dispersion factors and predator–prey oscillations. Hilgardia 27: 343–383

    Google Scholar 

  • Inouye BD (2005) Scaling up from local competition to regional coexistence across two scales of spatial heterogeneity: insect larvae in the fruits of Apeiba membranacea. Oecologia DOI 10.1007/s00442-005-0059-7

  • Ives AR, Foufopoulos J, Klopfer ED, Klug JL, Palmer TM (1996) Bottle or big-scale studies: how do we do ecology? Ecology 77:681–685

    Article  Google Scholar 

  • Kaiser H (1983) Small scale spatial heterogeneity influences predation success in an unexpected way: model experiments on the functional response of predatory mites (Acarina). Oecologia 56:249–256

    Article  Google Scholar 

  • Kareiva P (1989) Renewing the dialogue between theory and experiments in population ecology. In: Roughgarden J et al. (eds) Perspectives in ecological theory. Princeton University Press, Princeton, NJ, pp 68–88

    Google Scholar 

  • Kemp WM, Lewis MR, Cunningham FF, Stevenson JC, Boynton WR (1980) Microcosms, macrophytes, and hierarchies: environmental research in the Chesapeake Bay. In: Giesy JP (ed) Microcosms in ecological research. National Technical Information Service, Springfield, pp 911–936

    Google Scholar 

  • Langhaar HL (1951) Dimensional analysis and theory of models. Wiley, New York

    Google Scholar 

  • Lawton JH (1995) Ecological experiments with model systems. Science 269:328–331

    Article  PubMed  CAS  Google Scholar 

  • Lewis MR, Cullen JJ, Platt T (1984) Relationships between vertical mixing and photoadaptation of phytoplankton: similarity criteria. Mar Ecol-Progr Ser 15:141–149

    Article  Google Scholar 

  • Luckett C, Adey WH, Morrissey J, Spoon DM (1996) Coral reef mesocosms and microcosms—successes, problems and the future of laboratory models. Ecol Eng 6:57–72

    Article  Google Scholar 

  • Luckinbill LS (1973) Coexistence in laboratory populations of Paramecium aurelia and its predator Didinium nasutum. Ecology 54: 1320–1327

    Article  Google Scholar 

  • Margalef R (1967) Laboratory analogues of estuarine plankton systems. In: Lauff G (ed) Estuaries. American Association for the Advancement of Science, Washington, pp 515–521

    Google Scholar 

  • Melbourne BA, Chesson P (2005) Scaling up population dynamics: integrating theory and data. Oecologia DOI 10.1007/s00442-005-0058-8

  • Naeem S, Li S (1997) Biodiversity enhances ecosystem reliability. Nature 390: 507–509

    Article  CAS  Google Scholar 

  • Naeem S, Li SB (1998) Consumer species richness and autotrophic biomass. Ecology 79:2603–2615

    Article  Google Scholar 

  • Nixon SW, Alonso D, Pilson MEQ, Buckley BA (1980) Turbulent mixing in aquatic mesocosms. In: Giesy JP (ed) Microcosms in ecological research. National Technical Information Service, Springfield, pp 818–849

    Google Scholar 

  • Peeters JCH, Arts F, Escaravage V, Haas HA, de Jong JEA, van Loon R, Moest B, van der Put A (1993) Studies on light climate, mixing and reproducibility of ecosystem variables in mesocosms: consequences for the design. In: Peeters JCH et al.(eds) The impact of marine eutrophication on phytoplankton and benthic suspension feeders: results of a mesocosm pilot study. Eienst Getijde wateren, Middelburg, pp 7–23

    Google Scholar 

  • Persson L (1991) Behavioral-response to predators reverses the outcome of competition between prey species. Behav Ecol Sociobiol 28:101–105

    Article  Google Scholar 

  • Peters RH (1983) The ecological implications of body size. Cambridge University Press, Cambridge

    Google Scholar 

  • Petersen JE, Hastings A (2001) Dimensional approaches to scaling experimental ecosystems: designing mousetraps to catch elephants. Am Nat 157:324–333

    Article  CAS  PubMed  Google Scholar 

  • Petersen JE, Kemp W, Bartelson R, Boynton W, Chen C-C, Cornwell J, Gardner R, Hinkle D, Houde E, Malone T, Mowitt W, Murray L, Sanford L, Stevenson J, Sundberg K, Suttles S (2003) Multiscale experiments in coastal ecology: improving realism and advancing theory. Bioscience 53:1181–1197

    Article  Google Scholar 

  • Rijkeboer M, Gons HJ, Kromkamp J (1993) Preservation of the light-field in turbid lake and river water in laboratory-scale enclosure. J Plankton Res 15:517–530

    Article  Google Scholar 

  • Sanford LP (1997) Turbulent mixing in experimental ecosystem studies. Mar Ecol-Prog Ser 161: 265–293

    Article  CAS  Google Scholar 

  • Schindler DW (1998) Replication versus realism: the need for ecosystem-scale experiments. Ecosystems 1: 323–334

    Article  Google Scholar 

  • Schmitz OJ (2005) Scaling from plot experiments to landscapes: studying grasshoppers to inform forest ecosystem management. Oecologia DOI 10.1007/s00442-005-0063-y

  • Sheldon RW, Prakash A, Sutcliffe WHJ (1972) The size distribution of particles in the ocean. Limnol Oceangr 17:323–340

    Google Scholar 

  • Sugihara G, May RM (1990) Applications of fractals in ecology. Trends Ecol Evol 5:79–86

    Article  Google Scholar 

  • Thomas CD, Kunin WE (1999) The spatial structure of populations. J Anim Ecol 68:647–657

    Article  Google Scholar 

  • Turpin DH, Harrison PJ (1979) Limiting nutrient patchiness and its role in phytoplankton ecology. J Exp Mar Biol Ecol 39:151–166

    Article  CAS  Google Scholar 

  • West GB, Brown JH, Enquist BJ (1999) The fourth dimension of life: fractal geometry and allometric scaling of organisms. Science 284:1677–1679

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

John Petersen’s contributions to this work were funded by the U.S. EPA STAR program as part of the Multiscale Experimental Ecosystem Research Center (MEERC) at the University of Maryland Center for Environmental Science (Grant number R819640, Maryland U.S.A.). Travel funds were provided by Umeå University, Department of Ecology and Environmental Science (Umeå, Sweden). Many thanks to Allen Hastings, Michael Kemp and John Lawton for stimulating discussion that contributed to this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to John E. Petersen.

Additional information

Communicated by Craig Osenberg

Rights and permissions

Reprints and permissions

About this article

Cite this article

Petersen, J.E., Englund, G. Dimensional approaches to designing better experimental ecosystems: a practitioners guide with examples. Oecologia 145, 215–223 (2005). https://doi.org/10.1007/s00442-005-0062-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00442-005-0062-z

Keywords

Navigation