MEPS 252:61-76 (2003)  -  doi:10.3354/meps252061

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

Niall Broekhuizen1,*, John Oldman1, John Zeldis2

1National Institute of Water & Atmospheric Research Ltd., PO Box 11-115, Gate 10, Silverdale Road, Hamilton 2001, New Zealand
2National Institute of Water & Atmospheric Research Ltd., PO Box 8602, 10 Kyle Street, Riccarton, Christchurch 8001, New Zealand

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 cell¹s 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.


KEY WORDS: Lagrangian Ensemble method · Physiologically structured population models · Nutrient-phytoplankton dynamics · New Zealand continental shelf


Full text in pdf format