Abstract
Because the Lotka–Volterra competitive equations posit no specific competitive mechanisms, they are exceedingly general, and can theoretically approximate any underlying mechanism of competition near equilibrium. In practice, however, these models rarely generate accurate predictions in diverse communities. We propose that this difference between theory and practice may be caused by how uncertainty propagates through Lotka–Volterra systems. In approximating mechanistic relationships with Lotka–Volterra models, associations among parameters are lost, and small variation can correspond to large and unrealistic changes in predictions. We demonstrate that constraining Lotka–Volterra models using correlations among parameters expected from hypothesized underlying mechanisms can reintroduce some of the underlying structure imposed by those mechanisms, thereby improving model predictions by both reducing bias and increasing precision. Our results suggest that this hybrid approach may combine some of the generality of phenomenological models with the broader applicability and meaningful interpretability of mechanistic approaches. These methods could be useful in poorly understood systems for identifying important coexistence mechanisms, or for making more accurate predictions.
Original language | English (US) |
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Pages (from-to) | 35-44 |
Number of pages | 10 |
Journal | Theoretical Population Biology |
Volume | 123 |
DOIs | |
State | Published - Sep 2018 |
Bibliographical note
Funding Information:We thank E. Borer, G. Fury, F. Isbell, C. Lehman, D. Tilman, and D. Williams, and are grateful to our editor, P. Chesson, as well as reviewer G. Barabás and an anonymous reviewer for helpful comments on earlier drafts of this manuscript. A.T.C. was supported by a NSF Graduate Research Fellowship , base award number 00006595 , by a University of Minnesota Graduate Excellence Fellowship , and by an sDiv “catalyst” postdoctoral fellowship. Initial work by A.T.C. was conducted while in residence at the Sedgwick Reserve, hosted through the University of California Santa Barbara. Computing time was provided by the University of Minnesota Supercomputing Institute, iDiv, and UFZ.
Funding Information:
We thank E. Borer, G. Fury, F. Isbell, C. Lehman, D. Tilman, and D. Williams, and are grateful to our editor, P. Chesson, as well as reviewer G. Barab?s and an anonymous reviewer for helpful comments on earlier drafts of this manuscript. A.T.C. was supported by a NSF Graduate Research Fellowship, base award number 00006595, by a University of Minnesota Graduate Excellence Fellowship, and by an sDiv ?catalyst? postdoctoral fellowship. Initial work by A.T.C. was conducted while in residence at the Sedgwick Reserve, hosted through the University of California Santa Barbara. Computing time was provided by the University of Minnesota Supercomputing Institute, iDiv, and UFZ.
Publisher Copyright:
© 2018 Elsevier Inc.
Keywords
- Interspecific competition
- Interspecific tradeoff
- Lotka–Volterra competitive equations
- Model abstraction
- Model uncertainty
- Process noise