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JOURNAL OF PLANKTON RESEARCH | VOLUME 17 | NUMBER 11 | PAGES 2093-2115 | 1995
© Oxford University Press

research-article

Zooplankton cohort analysis using systems identification techniques

Robert Jellison1,3, Gayle L. Dana1,4 and John M. Melack1,2

1Marine Science Institute, University of California Santa Barbara, CA 93106, USA 2Department of Biological Sciences, University of California Santa Barbara, CA 93106, USA 3Persent address: Sierra Nevada Aquatic Research Laboratory, University of California Star Rt. 1, Box 198, Mammoth Lakes, CA 93546, USA 4Present address: Biological Sciences Center, Desert Research Institute, University of Nevada PO Box 60220, Reno, NV 89506-0220, USA

Received on June 21, 1994; accepted on March 21, 1995 The linear-transfer and lag-Manly models of zooplankton cohort development were examined using data generated from a third more realistic model. The more realistic multi-transfer model included variance in development rate among individuals. The linear-transfer model produced highly biased estimates of development rate under conditions of rapidly changing recruitment. Although its performance was improved by increasing the number of modeled stages and thus decreasing the rate of change in recruitment compared to stage duration, a positive bias remained. The lag-Manly model also produced positively biased estimates of stage duration given non-zero variance in development rates. A comparison of the models' performances under different simulated sampling regimes recommended the multi-transfer model. Use of the multi-transfer model was illustrated by determining the development and mortality rates of the brine shrimp, Artemia monica reared under three different conditions of food and temperature corresponding to natural regimes in Mono Lake, California. The experimental conditions and sampling regime resulted in high relative standard errors (mean, 33%) in stage abundance estimates not atypical of zooplankton sampling regimes in lakes. A Monte Carlo analysis was used to determine the uncertainty in estimated parameters and determine the level of stage aggregation which maximized the amount of information derived from the experiments.


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