Statistical error model comparison for logistic growth of green algae (Raphidocelis subcapitata). (February 2017)

• Record Type: Journal Article
• Authors: Banks, H.T.; Collins, Elizabeth; Flores, Kevin; Pershad, Prayag; Stemkovski, Michael; Stephenson, Lyric
• Is Part Of: Applied mathematics letters. Volume 64(2017:Feb.)
• Page Start: 213
• Page End: 222
• Publication Date: 2017-02
• Subjects: Algae growth models -- Parameter estimation -- Uncertainty quantification -- Asymptotic theory; Applied mathematics -- Periodicals; 519.05
• DOI: 10.1016/j.aml.2016.09.006
• Languages: English
• ISSNs: 0893-9659

Abstract

Abstract: 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.