Estimation of sticking and contact efficiencies in aggregation of phytoplankton: The 1993 SIGMA tank experiment

The coagulation of phytoplankton, a fundamental mechanism for vertical flux in the oceans and a possible predatory escape mechanism, is a function of the density of suspended particles and at least three enigmatic system processes: the encounter rate of the particles; the contact efficiency of various sized particles upon encounter; and the efficiency of sticking upon contact. A variant of a continuous coagulation model, including the second-order aggregation rate representation and data obtained from the 1993 SIGMA tank experiment at Santa Barbara, are used first to estimate ‘stickiness’, the efficiency of sticking given that a collision occurs. Primary tools are an inverse, least squares methodology, an aggregation model, and an individual growth model for phytoplankton. The model output corresponds well with the data for smaller sized particles (<0.4 mm3); however, predicted densities for larger particles were less than observed, and the predicted timing of the bloom was earlier than observed. These anomalies led to an investigation of the interacting roles of stickiness, contact efficiency, and nutrient storage in individual cells. The analyses suggest that (i) aggregation is relatively insensitive to the sticking efficiency, and thus it is difficult to estimate stickiness accurately by fitting aggregation data. (ii) contact efficiency appears to be more functionally variable than assumed in traditional representations, and estimating contact efficiency, jointly with the sticking efficiency, generally produces better agreement with the SIGMA experimental size particle data spectrum; (iii) for the SIGMA tank environment, estimates of contact efficiency are dimensionally more closely related to diameter than to the traditional surface area representation; and (iv) stored nutrient reserves may play a fundamental role in governing timing of peak algal bloom and dynamics of aggregates; inclusion of nutrient storage improves estimation of peak bloom, but does not significantly improve prediction of aggregate dynamics in the SIGMA tank experiment.