Time‐series Analysis in Ecology

Abstract

Most phenomena in ecosystem research are assessed via repeated measurements of environmental variables. The dynamics of these time series are investigated with a variety of statistical techniques and nonlinear methods which allow the separation of short‐ and long‐term components, show all types of trends and quantify the information contained and the complexity of the data sets.

Keywords: time series; ecosystems; nonlinear methods; short‐ and long‐term dynamics

Figure 1.

Cross‐correlation functions of the SO4 time series in runoff from a small stream in Germany (Lehstenbach, Northern Bavaria), with four other variables measured in the same catchment.

Figure 2.

Wavelet analysis for the runoff of the Danube river at the Achleiten weir Germany (1901–2003). The power coefficients are connected by isolines (11 different levels on a logarithmic scale) and the isolines are colour‐coded (blue is low values, red is high values).

Figure 3.

Mean Information Gain and Fluctuation Complexity for a set of 1571 runoff time series (Hydro‐Climatic Data Network, US Geological Survey). The majority fit well to a one‐parametric curve, the Rényi complexity. The maximum complexity at a given MIG is given by the Bernoulli limit (black curve).

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Further Reading

Brockwell P and Davis R (1987) Time Series: Theory and Methods. Berlin: Springer.

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Priestley MB (1988) Non‐linear and Non‐stationary Time Series Analysis. London: Academic Press.

Pollock DSG (1999) A Handbook of Time Series Analysis, Signal Processing and Dynamics. San Diego: Academic Press.

Schroeder MR (1991) Fractals, Chaos, Power Laws – Minutes from an Infinite Paradise. New York: WH Freeman.

Tong H (1990) Non‐linear Time Series Analysis. Oxford: Oxford University Press.

Wei WWS (1994) Time Series Analysis – Univariate and Multivariate Methods. Redwood City: Addison‐Wesley.

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Lange, Holger(Jan 2006) Time‐series Analysis in Ecology. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1038/npg.els.0003276]