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The aim of this paper is to identify significant changes, otherwise known as change-points, in short time series. The practical application that motivated this work concerns the detection of change-point differences in behavioural annual routines of living organisms (i.e. how they schedule activities in a regular way over the year). Our study case focuses on the annual routine of a specialist river bird species, the European Dipper (Cinclus cinclus), coping with drastic environmental changes in flow regime impacting their life-history stages. We compare behavioural data collected on a monthly basis in natural and altered rivers; they result in 12 values of so-called time-activity budgets expressed as mean percentage of time spent in each of 5 different behavioural activities. The methods used in this paper are based on statistical tests for detecting one or more points of breaks in time series. Different methods are compared on this first dataset : • The method of A. N. Pettitt (1979) which is based on the Mann and Whitney statistical test for comparing two samples thus 2 segmentations of the original time series; • The method of P. Hubert et al. (1989) for which the test statistic considers the squared difference between the series and a fixed segmentation. In this algorithm all possible segmentations of a time series are tested and the optimal segmentation is selected using the method of contrasts introduced by Scheffe (1959);

chan, f., marinova, d. and anderssen, r.s. (eds) modsim2011, 19th international congress on modelling and simulation.

Detection of significant changes in short time series: applications to the analysis of annual routines in behavioural ecology and to the analysis of breaks in abundance