Advertisement

Community Ecology

, Volume 19, Issue 2, pp 199–202 | Cite as

Can we make quantitative predictions for relative yield with incomplete knowledge of model parameters?

Article
  • 3 Downloads

Abstract

A main limitation in community ecology for making quantitative predictions on species abundances is often an incomplete knowledge of the parameters of the population dynamics models. The simplest linear Lotka-Volterra competition equations (LLVCE) for S species require S2 parameters to solve for equilibrium abundances. The same order of experiments are required to estimate these parameters, namely the carrying capacities (from monoculture experiments) and the competition coefficients (from biculture or pairwise experiments in addition to monoculture ones). For communities with large species richness S it is practically impossible to perform all these experiments. Therefore, with an incomplete knowledge of model parameters it seems more reasonable to attempt to predict aggregated or mean quantities, defined for the whole community of competing species, rather than making more detailed predictions, like the abundance of each species. Here we test a recently derived analytical approximation for predicting the Relative Yield Total (RYT) and the Mean Relative Yield (MRY) as functions of the mean value of the interspecific competition matrix a and the species richness S. These formulae with only a fraction of the model parameters, are able to predict accurately empirical measurements covering a wide variety of taxa such as algae, vascular plants, protozoa. We discuss the dependence of these global community quantities on the species richness and the intensity of competition and possible applications are pointed out.

Key words

Biodiversity-ecosystem functioning experiments Quantitative Lotka-Volterra competition theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgments

Work supported in part by ANII-Uruguay (SNI and project ERANET-LACR&I2016-1005422).

References

  1. Cardinale, B.J. et al. 2012. Biodiversity loss and its impact on humanity. Nature 486:59–67.CrossRefGoogle Scholar
  2. Fort, H. 2018. Quantitative predictions from competition theory with an incomplete knowledge of model parameters tested against experiments across diverse taxa. Ecol. Model. 368:104–110.CrossRefGoogle Scholar
  3. Fort, H., Dieguez, F., Halty, V. and Soares-Lima, J.M. 2017. Two examples of application of ecological modeling to agricultural production: Extensive livestock farming and overyielding in grassland mixtures. Ecol. Model. 357:23–34.CrossRefGoogle Scholar
  4. Fort, H. and Mungan, M. 2015. Predicting abundances of plants and pollinators using a simple compartmental mutualistic model. Proc. Royal Soc. B282, 1808.Google Scholar
  5. Fort, H. and Segura, A. 2018. Competition across diverse taxa: quantitative integration of theory and empirical research using global indices of competition. Oikos, DOI: 10.1111/oik.04756.Google Scholar
  6. Fort, H., Vázquez, D.P. and Lan, B.L. 2016. Abundance and generalisation in mutualistic networks: solving the chicken-and-egg dilemma. Ecol. Lett. 19:4–11.CrossRefGoogle Scholar
  7. Halty, V., Valdés, M., Tejera, M., Picasso, V. and Fort, H. 2017. Modelling plant interspecific interactions from experiments of perennial crop mixtures to predict optimal combinations. Ecol. Appl. 27:2277–2289.CrossRefGoogle Scholar
  8. Hector, A. et al. 2010. The data sets used in the paper with detailed descriptions. Ecological Archives E091-155-S1, Suppl. to Ecology 91:2213-2220. http://www.esapubs.org/archive/ecol/E091/155/
  9. Hubbel, S.P. 2002. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton Univ. Press, Princeton, NJ.Google Scholar
  10. Loreau, M. and Hector, A. 2001 Partitioning selection and complementarity in biodiversity experiments. Nature 412:72–76.CrossRefGoogle Scholar
  11. Lotka, A.J. 1925. Elements of Physical Biology. Williams and Wilkins, Baltimore.Google Scholar
  12. Segura, A.M., Kruk, C., Calliari, D. and Fort, H. 2013. Use of a morphology-based functional approach to model phytoplankton community succession in a shallow subtropical lake. Freshwater Biol. 58:504–512.CrossRefGoogle Scholar
  13. Vandermeer, J.H. 1989 The Ecology of Intercropping. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  14. Volterra, V. 1926. Fluctuations in the abundance of a species considered mathematically. Nature 118:558–560.CrossRefGoogle Scholar
  15. de Wit, C.T. 1970. On the modelling of competitive phenomena. Proc. Adv. Study Inst. Dynamics Numbers Popul. Oosterbeek, 1970:269–281.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2018

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Institute of Physics, Faculty of ScienceUniversidad de la RepúblicaMontevideoUruguay

Personalised recommendations