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1 August 2014 A Likelihood-Based Biostatistical Model for Analyzing Consumer Movement in Simultaneous Choice Experiments
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Consumer feeding preference among resource choices has critical implications for basic ecological and evolutionary processes, and can be highly relevant to applied problems such as ecological risk assessment and invasion biology. Within consumer choice experiments, also known as feeding preference or cafeteria experiments, measures of relative consumption and measures of consumer movement can provide distinct and complementary insights into the strength, causes, and consequences of preference. Despite the distinct value of inferring preference from measures of consumer movement, rigorous and biologically relevant analytical methods are lacking. We describe a simple, likelihood-based, biostatistical model for analyzing the transient dynamics of consumer movement in a paired-choice experiment. With experimental data consisting of repeated discrete measures of consumer location, the model can be used to estimate constant consumer attraction and leaving rates for two food choices, and differences in choice-specific attraction and leaving rates can be tested using model selection. The model enables calculation of transient and equilibrial probabilities of consumer-resource association, which could be incorporated into larger scale movement models. We explore the effect of experimental design on parameter estimation through stochastic simulation and describe methods to check that data meet model assumptions. Using a dataset of modest sample size, we illustrate the use of the model to draw inferences on consumer preference as well as underlying behavioral mechanisms. Finally, we include a user's guide and computer code scripts in R to facilitate use of the model by other researchers.
Adam R. Zeilinger, Dawn M. Olson and David A. Andow "A Likelihood-Based Biostatistical Model for Analyzing Consumer Movement in Simultaneous Choice Experiments," Environmental Entomology 43(4), (1 August 2014).
Received: 11 October 2013; Accepted: 1 June 2014; Published: 1 August 2014

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