Algal modelling

Most studies with phytoplankton cultures, one way or the other, end up collecting time-series observations of population densities over time. Then, these data are used to extract information such as the maximum growth rate or the carrying capacity of a species. But there is so much more we can do...

Time-series analysis are on the forefronts of statistics. These techniques deal with observations that are correlated in time. As such, these data cannot meet the widespread assumption of independency among datapoints.


During my PhD I used dynamic models to analyse phytoplankton time-series. These techniques allow to go far beyond estimating traditional demographic parameters. You can (i) build your own model (based on a-priori understanding of the specific study system), (ii) study the direction and the strength of the correlation, and (iii) account for different sources of uncertainties.

Check out chapter 11 in Ben Bolker's book (Ecological Models and Data in R)


My articles introduce two new models: (1) nitrate-nitrite-phytoplankton and (2) nitrate-ammonium-phytoplankton



Limnology & Oceanography (2012)

Nitrate-Nitrite-Phytoplankton model

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Functional Ecology (2016)

Nitrate-Ammonium-Phytoplankton with size-specific parameters

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Ecological Modelling (2016)

Nitrate-Ammonium-Phytoplankton model

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J. of Theoretical Biology (2016)

Nitrate-Phytoplankton Model

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Deakin University,

Burwood VIC 3120, Australia

©2017 by Martino Malerba.