Sub-grid-scale differences between individuals influence simulated phytoplankton production and biomass in a shelf-sea system

Authored by N Broekhuizen, J Oldman, J Zeldis

Date Published: 2003

DOI: 10.3354/meps252061

Sponsors: New Zealand Foundation for Research Science and Technology

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

In reality, individuals differ from one another. Some of this can be attributed to genetic differences, but much is due to environmental effects. Even neighbouring cells will have differing histories and may be in differing physiological condition in consequence. Many of the processes governing cell-growth are non-linear functions of the cell's physiological state. This, together with the possibility that each cell will be in a unique physiological state, implies that it is not possible reliably to infer the population-level growth rate from the product of population abundance and an individual growth-rate derived on the basis of the average physiological characteristics of the local population. Unfortunately, this is precisely the assumption that is implicit in the vast majority of phytoplankton models-which take no account of local-scale physiological structure in the phytoplankton population. Here, we present an individual-based population model of phytoplankton dynamics. This model utilises the Lagrangian Ensemble method to take account of local-scale physiological structure in the population. We make comparisons of the predictions of this model when run as a truly individual-based model or in a manner mimicking a model having no representation of local-scale population physiological structure. The results suggest that, under realistic environmental conditions, individuals in close proximity to one another can indeed be in substantially different physiological condition. More importantly, failure to take proper account of this variability results in differences of more than 30 \% between the predictions of standing crop and productivity made by the structured-model descriptions of the same underlying biology.

Tags

Water Rates New-zealand Volume Ocean Mixed-layer Lagrangian model Marine diatoms Uptake kinetics Silicon