TL;DR: Analysis methods which reduce biological data such as species abundances to a more useable form, including ordination, classification and diversity index methods, are critically reviewed and application to pollution studies is discussed.
TL;DR: Various approaches to the elucidation of periodicities in biological data sets are reviewed and evaluated and the correlogram obtained from autocorrelation analyses is used.
Abstract: Various approaches to the elucidation of periodicities in biological data sets are reviewed and evaluated. The major periodicity in a data set can be revealed by the correlogram obtained from autocorrelation analyses; knowing the main wavelength from autocorrelation, the proportion of the total variation due to a sinusoidal curve is readily obtained by multiple regression. Copyright
TL;DR: In this paper, the authors considered the building of stochastic models and the related analysis of discrete data in two biological problems, the first arises from the reproduction of yeast cells, while the second is concerned with the aggregation of nucleoli.
Abstract: This paper considers the building of stochastic models and the related analysis of discrete data in two biological problems, The first arises from the reproduction of yeast cells, while the secondis concerned with the aggregation of nucleoli. Galton-Watson and aggregation models are constructed for the respective processes and their goodness of fit to the data tested