| Statistical Programs |
| College of Agricultural and Life Sciences |
University of Idaho |
| Seminar Announcement |
| "Applied Statistics in Agriculture" |
|
Gause’s principle of competitive coexistence? Further fun with the “data cloning” algorithm for maximum likelihood estimation in hierarchical statistical models
Presented By |
| Dr. Brian C. Dennis |
|
Department of Fish and Wildlife Resources and Department of Statistics University of Idaho |
| Tuesday, April 14 3:30 P. M. Ag. Science 62 |
|
Gause’s (1934) experiments on competing laboratory populations of competing species of of
protozooans have been icons in ecology textbooks for generations. One experiment in particular pitted Paramecium aurelia
against P. caudatum in three replicate laboratory cultures. Although both species were present in all three cultures upon
the experiment’s termination, Gause’s makeshift parameter estimates for the Lotka-Volterra competition model suggested that
P. aurelia was well on its way to competitive victory in all three cultures. The resulting
Principle of Competitive Exclusion of one species by a close competitor has infused ecological and evolutionary thought ever since.
Astonishingly, Gause’s original data have not received much analysis to date in the ecological literature. Until recently, calculating
the likelihood functions for realistic “state space” models accounting for both sampling and natural variability has not been computationally feasible. The present study used the new “data cloning” algorithm (Lele et al. 2007 Ecology Letters) to conduct statistical inferences for Gause’s P. aurelia v. P. caudatum experiment. The inferences are based on a full state-space competition model featuring both sampling error and process noise. Maximum likelihood parameter estimates put the populations in a dynamical region of coexistence, that is, the estimated model predicts that the populations will ultimately attain a stable positive equilibrium. Adequate modeling of the stochastic variability as well as the deterministic forces is critical for understanding population dynamical behavior. All interested faculty, staff, and graduate students are invited to attend. |
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