Abstract
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.
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References
Alvarez, M. C. & L. A. Fuiman, 2003. Exposure to atrazine at environmentally realistic levels affects survival potential of a marine fish larva. Poster presentation, SETAC meeting, Austin.
Baumont, G., F. Ménage, J. R. Schneiter, A. Spurgin & A. Vogel, 2000. Quantifying human and organizational factors in accident management using decision trees: the HORAAM method. Reliability Engineering and System Safety 70: 113–124.
Berry, M. J. & G. S. Linoff, 1999. Mastering Data Mining: The Art and Science of Customer Relationship Management. Wiley, New York.
Bohren, B. F., M. Hadzikadic & E. N. Hanley Jr., 1995. Extracting knowledge from large medical databases: an automated approach. Computers and Biomedical Research 28: 191–210.
Buskey, E. J., 1984. Swimming pattern as an indicator of the roles of copepod sensory systems in the recognition of food. Marine Biology 79: 165–175.
Chae, Y. M., H. S. Kim, K. C. Tark, H. J. Park & S. H. Ho, 2003. Analysis of healthcare quality indicator using data mining and decision support system. Expert Systems with Applications 24: 167–172.
Cox, B., T. Kislinger & A. Emili, 2005. Integrating gene and protein expression data: pattern analysis and profile mining. Methods 35: 303–314.
Chang, Y. C., P. C. Lai & M. T. Lee, 2007. An integrated approach for operational knowledge acquisition of refuse incinerators. Expert Systems with Applications 33: 413–419.
DeVantier, L. M., G. De’Ath, T. J. Done & E. Turak, 1998. Ecological assessment of a complex natural system: a case study from the Great Barrier Reef. Ecological Applications 8: 480–496.
Erlandsson, J. & V. Kostylev, 1995. Trail following, speed and fractal dimension of movement in a marine prosobranch, Littorina littorea, during a mating and a non-mating season. Marine Biology 122: 87–94.
Giordana, A. & F. Neri, 1995. Search-intensive concept induction. Evolutionary Computation 3: 375–416.
Greene, D. P. & S. F. Smith, 1993. Competition-based induction of decision models from examples. Machine Learning 13: 229–257.
Han, J. & M. Kamber, 2001. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, Cambridge.
Jakobsen, H. H., E. Halvorsen, B. W. Hansen & A. W. Visser, 2005. Effects of prey motility and concentration on feeding in Acartia tonsa and Temora longicornis: the importance of feeding modes. Journal of Plankton Research 27: 775–785.
Jourdam, L., C. Dhaenens, E. G. Talbi & S. Gallina, 2002. A data mining approach to discover genetic and environmental factors involved in multifactorial diseases. Knowledge-Based Systems 15: 235–242.
Kirchner, K., K. H. Tölle & J. Krieter, 2004. The analysis of simulated sow herd datasets using decision tree technique. Computers and Electronics in Agriculture 42: 111–127.
Kudo, M. & J. Skalansky, 2000. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33: 25–41.
Lindsay, S. M. & R. G. Vogt, 2004. Behavioral responses of newly hatched zebrafish (Danio rerio) to amino acid chemostimulants. Chemical Senses 29: 93–100.
MacQueen, J., 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley symposium on mathematical statistics and probability, Vol. 1. University of California Press, Berkeley: 281–297.
Mandelbrot, B. B., 1982. The Fractal Geometry of Nature. W.H. Freeman and Company, San Francisco.
Mandelbrot, B. B., 1967. How long is the Coast of Britain? Statistical self-similarity and fractal dimension. Science 155: 636–638.
Mitchell, R. S., R. A. Sherlock & L. A. Smith, 1996. An investigation into the use of machine learning for determining estrus in cows. Computers and Electronics in Agriculture 15: 195–213.
Nagelkerken, I., G. Velde, M. W. Gorissen, G. J. Meijer, T. Hof & C. Hartog, 2000. Importance of mangroves, seagrass beds and the shallow coral reef as a nursery for important coral reef fishes, using a visual census technique. Estuarine, Coastal and Shelf Science 51: 31–44.
Pasternak, Z., B. Blasius, A. Abelson & Y. Achituv, 2006. Host-finding behaviour and navigation capabilities of symbiotic zooxanthellae. Coral Reefs 25: 201–207.
Quinlan, J. R., 1986. Induction of decision trees. Machine Learning 1: 81–106.
Seuront, L., J. S. Hwang, L. C. Tseng, F. G. Schmitt, S. Souissi & C. K. Wong, 2004. Individual variability in the swimming behavior of the sub-tropical copepod Oncaea venusta (Copepoda: Poecilostomatoida). Marine Ecology Progress Series 283: 199–217.
Traniello, J. F. A., 1989. Foraging strategies of ants. Annual Review of Entomology 34: 191–210.
Ueda, T., S. Koya & Y. K. Maruyama, 1999. Dynamic patterns in the locomotion and feeding behaviors by the placozoan Trichoplax adhaerence. BioSystems 54: 65–70.
Vandromme, P., F. G. Schmitt, S. Souissi, E. J. Buskey, J. R. Strickler, C.-H. Wu & J.-S. Hwang, 2010. Symbolic analysis of plankton swimming trajectories: case study of Strobilidium sp. (Protista) helical walking under various food conditions. Zoological Studies 49(3): 289–303.
Visser, A. W. & U. H. Thygesen, 2003. Random motility of plankton: diffusive and aggregative contributions. Jounral of Plankton Research 25: 1157–1168.
Visser, A. W. & T. Kiørboe, 2006. Plankton motility patterns and encounter rates. Oecologia 148: 538–546.
Witten, I. H. & E. Frank, 2005. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, San Francisco.
Wu, C.-H., H.-U. Dahms, E. J. Buskey, J. R. Strickler & J.-S. Hwang, 2010. Behavioral interactions of the copepod Temora turbinata with potential ciliate prey. Zoological Studies 49(2): 157–168.
Yen, J., 1988. Directionality and swimming speeds in predator-prey and male-female interactions of Euchaeta rimana, a subtropical marine copepod. Bulletin of Marine Science 43: 395–403.
Acknowledgments
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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Guest editors: J.-S. Hwang and K. Martens / Zooplankton Behavior and Ecology
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Chang, YC., Yan, JC., Hwang, JS. et al. Data-oriented analyses of ciliate foraging behaviors. Hydrobiologia 666, 223–237 (2011). https://doi.org/10.1007/s10750-010-0548-5
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DOI: https://doi.org/10.1007/s10750-010-0548-5