Data-oriented analyses of ciliate foraging behaviors
Optimal foraging theory states that natural selection makes foragers efficient food harvesters and maximizing a colony’s energy intake. This study presumed that the ciliates foraging trajectories follow optimal foraging theory, verified the presumption and discover specific rules and patterns hidden in the ciliate’s trajectories data using methodologies of statistical, cluster analyses, and decision tree analysis. This study examined the foraging behaviors of ciliates by video recordings and quantitative analyses of movement trajectories under four nourishment conditions (low, medium, high, and highest concentrations). Similar biological studies adopt statistical analyses to certain locomotion indices to determine the responses of plankton to various aquatic environments. In addition to statistical analyses, cluster analysis was used in this study to confirm the observations of the statistical analyses. The statistical analysis and cluster analysis results in this study revealed two distinct groups of trajectories or behaviors, which matched the optimal foraging theory. Decision tree analysis was then applied to acquire objective information regarding foraging behaviors, and further detailed the foraging behaviors with explicit classification rules using locomotion indices. The production rules can play an alternative role to assess the sustainability of an aquatic environment in terms of algae concentration.
KeywordsCiliate Statistical analysis Locomotion index Clustering Decision tree analysis Optimal foraging theory
The authors would like to thank the editors and reviewers for their helpful comments. This research was supported by grants NSC 98-2621-B-019-001-MY3 and NSC 99-2611-M-019-009 from the National Science Council and from the National Taiwan Ocean University (CMBB 97529002A9). The authors also thank Professors J. Rudi Strickler from University of Wisconsin, Milwaukee, USA, and Edward J. Buskey from University of Texas at Austin, USA for their assistance at various stages of the experiment and for constructive suggestions regarding the experiments.
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