Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata)

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• 作者：H.T. Banks ; ^{htbanks@ncsu.edu" class="auth_mail" title="E-mail the corresponding author} ; Elizabeth Collins ; Kevin Flores ; Prayag Pershad ; Michael Stemkovski ; Lyric Stephenson
• 关键词：Algae growth models ; Parameter estimation ; Uncertainty quantification ; Asymptotic theory
• 刊名：Applied Mathematics Letters
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：213-222
• 全文大小：1677 K

文摘

We validate a model for the population dynamics, as they occur in a chemostat environment, of the green algae Raphidocelis subcapitata, a species that is often used as a primary food source in toxicity experiments for the fresh water crustacean Daphnia magna. We collected longitudinal data from 4 replicate population experiments with R. subcapitata. This data was fit to a logistic growth model to reveal patterns of the algae growth in a continuous culture. Overall, our results suggest that a proportional error statistical model is the most appropriate for logistic growth modeling of R. subcapitata continuous population growth.