An application of two techniques for the analysis of short, multivariate non-stationary time-series of Mauritanian trawl survey data

Abstract

Abstract Min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) are complementary techniques for analysing short (>15–25 y), non-stationary, multivariate data sets. We illustrate the two techniques using catch rate (cpue) time-series (1982–2001) for 17 species caught during trawl surveys off Mauritania, with the NAO index, an upwelling index, sea surface temperature, and an index of fishing effort as explanatory variables. Both techniques gave coherent results, the most important common trend being a decrease in cpue during the latter half of the time-series, and the next important being an increase during the first half. A DFA model with SST and UPW as explanatory variables and two common trends gave good fits to most of the cpue time-series.