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DETECTING SEASONALITY USING TIME SERIES ANALYSIS: COMPARING FORAMINIFERAL POPULATION DYNAMICS WITH RAINFALL DATA

Petroleum Geoscience Programme, Department of Chemical Engineering, The University of West Indies, St Augustine, Trinidad, West Indies

^* bwilson{at}eng.uwi.tt

Foraminiferal population densities vary over time. They are sometimes monitored to assess seasonality, and sometimes to assess the impact of anthropogenic activities. The resulting temporal record, which constitutes a time series, must be carefully analyzed to ensure that the observed fluctuations are truly natural, seasonal phenomena, and not due to anthropogenic causes, before drawing final conclusions. In this paper we use autocorrelation, a mathematical facet of time series analysis (TSA) that unambiguously identifies seasonality. The analyses were conducted using the Statistix 2.0 statistical package, and data normalized by transforming to ln(y), where y is the population size as a function of time to ensure proper percentage error representation. Using as a data set the monsoonal rainfall patterns on Trinidad, West Indies, we show that autocorrelograms for seasonal time series comprise a sine-like wave that fluctuates around zero. TSA is then used to examine seasonality in the population dynamics of Globigerina bulloides in the Cariaco Basin, Venezuela, Glabratella ornatissima off California, and Quinqueloculina in the Indian River Lagoon, Florida. These examples suggest that TSA can be a useful tool in identifying seasonality effects in foraminiferal population dynamics.

This article has been cited by other articles:

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