Predicting the Outcome of Competition Using Experimental Data: Maximum Likelihood and Bayesian Approaches

摘要: Lotka-Volterra (LV) equations have been used extensively to explore the possible dynamic outcomes of interspecific competition. But while there hundreds papers on mathematical properties models, only a handful that techniques for fitting these models actual data, and no interface experimental design statistical inference when LV census data. In this paper we present analysis Gause's cultures Paramecium aurelia P. caudatum, using analytical methods based maximum likelihood Bayesian statistics. We compare effectiveness two approaches in addressing several questions about competition from data : Are mutual effects competing populations substantial ? competitive symmetrical expected coexist or eliminate each other by show even laboratory-derived set with minimal variability can entail significant levels uncertainty nature interaction. assess errors involved estimating strength symmetry competition, find one's conclusions depend critically assumptions sources also estimate probabilities alternative behaviors species. use simulations evaluate how particular designs might improve our power characterize outcome much more information is gained running experiments at different starting conditions than replicating same experiment condition.