Clark Adam Thomas, Neuhauser Claudia
University of Minnesota, Department of Ecology, Evolution, and Behavior, 1987 Upper Buford Circle, Saint Paul, MN 55108, USA; Department of Physiological Diversity, Helmholtz Center for Environmental Research (UFZ), Permoserstrasse 15, Leipzig 04318, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Synthesis Centre for Biodiversity Sciences (sDiv), Deutscher Platz 5e, 04103, Leipzig, Germany; Leipzig University, Ritterstrasse 26, 04109 Leipzig, Germany.
University of Minnesota, Department of Ecology, Evolution, and Behavior, 1987 Upper Buford Circle, Saint Paul, MN 55108, USA; University of Minnesota, University of Minnesota Informatics Institute, Minneapolis, MN, 55455, USA; Division of Research, University of Houston, Houston, TX 77204, United States.
Theor Popul Biol. 2018 Sep;123:35-44. doi: 10.1016/j.tpb.2018.05.002. Epub 2018 May 31.
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.
由于洛特卡-沃尔泰拉竞争方程没有设定具体的竞争机制，它们极为通用，并且在理论上可以近似平衡附近的任何潜在竞争机制。然而，在实际中，这些模型在多样的群落中很少能产生准确的预测。我们认为，理论与实践之间的这种差异可能是由不确定性在洛特卡-沃尔泰拉系统中的传播方式导致的。在用洛特卡-沃尔泰拉模型近似机制关系时，参数之间的关联会丢失，并且小的变化可能对应于预测中巨大且不现实的变化。我们证明，利用假设的潜在机制所预期的参数之间的相关性来约束洛特卡-沃尔泰拉模型，可以重新引入这些机制所施加的一些潜在结构，从而通过减少偏差和提高精度来改进模型预测。我们的结果表明，这种混合方法可能将现象学模型的一些通用性与机制方法更广泛的适用性和有意义的可解释性结合起来。这些方法在理解不足的系统中可能有助于识别重要的共存机制，或做出更准确的预测。
