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Quantitative predictions from competition theory with an incomplete knowledge of model parameters tested against experiments across diverse taxa

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  • Fort, Hugo

Abstract

The capacity of community ecology for making quantitative predictions is often limited by incomplete empirical information which precludes obtaining reasonable estimates of model parameters. This is particularly the case for communities with large species richness S since it is practically impossible to perform the S monoculture experiments (to obtain the species carrying capacities) plus the S×(S−1)/2 pairwise experiments (required to estimate the entire set of interspecific interaction coefficients of the interaction or community matrix). However quantitative predictive tools are vital for understanding the fate of ecological communities.

Suggested Citation

  • Fort, Hugo, 2018. "Quantitative predictions from competition theory with an incomplete knowledge of model parameters tested against experiments across diverse taxa," Ecological Modelling, Elsevier, vol. 368(C), pages 104-110.
    • Handle: RePEc:eee:ecomod:v:368:y:2018:i:c:p:104-110
    DOI: 10.1016/j.ecolmodel.2017.11.002
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    References listed on IDEAS

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    1. Fort, Hugo & Dieguez, Francisco & Halty, Virginia & Lima, Juan Manuel Soares, 2017. "Two examples of application of ecological modeling to agricultural production: Extensive livestock farming and overyielding in grassland mixtures," Ecological Modelling, Elsevier, vol. 357(C), pages 23-34.
    2. Michel Loreau & Andy Hector, 2001. "Partitioning selection and complementarity in biodiversity experiments," Nature, Nature, vol. 412(6842), pages 72-76, July.
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    1. Fort, Hugo, 2018. "On predicting species yields in multispecies communities: Quantifying the accuracy of the linear Lotka-Volterra generalized model," Ecological Modelling, Elsevier, vol. 387(C), pages 154-162.
    2. Fort, Hugo, 2020. "Making quantitative predictions on the yield of a species immersed in a multispecies community: The focal species method," Ecological Modelling, Elsevier, vol. 430(C).

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