NONLINEAR-ANALYSIS OF CHLOROPHYLL-A TRANSECTS AS A METHOD OF QUANTIFYING SPATIAL STRUCTURE

A method of analysis, initially developed as a method of distinguishin g between chaotic dynamics and observational noise in time series, was applied to transects of chlorophyll a from the Southern Ocean. The al gorithm works by predicting the behaviour of a data set based on patte rns present within the data. This differs from previous analyses in th at it permits classification of the dynamics governing the system. In a chaotic system, predictions become exponentially poor as one attempt s to predict further ahead, due to the sensitivity of the system to in itial conditions. However, in a system governed by stochastic noise, n o such deterioration in predictions is observed. This work represents the first application of the algorithm to oceanic transect data, and o ur results show a less than exponential decline in predictive ability. The behaviour of the prediction curves is closely related to that of the corresponding autocorrelation functions, indicative of a stochasti c, but spatially coherent data set with significant positive autocorre lations. Furthermore, successful prediction is correlated with the sum of the chlorophyll power spectrum. We conclude that the predictive ab ility of the algorithm is greatest when spatial variation in the chlor ophyll transects is high.