Abstract
Succession theories and related methods for time series analysis are presented and illustrated by examples using published data. Starting from a rather general definition of succession which is understood as any directional change of vegetation in time, the phenomena can be distinguished at three different levels of perception: pattern, process and mechanism. Pattern is the phenomenon perceived and reflected in the sample data. Process is a grammar generating this pattern (Dale 1980). Both can be accessed by appropriate statistical analysis. Mechanisms are the causes of the changes. Whereas in some cases they can be inferred from patterns and processes, they cannot be observed or measured directly.
The methods and examples described include the statistical design of long-term investigations, the analysis of community dynamics using Markov chains, pattern analysis by ordination, the analysis of temporal autocorrelation and fuzzy ordination for fitting time series data to a time vector. In most methods, the results can be tested for the existence of trends using Monte Carlo simulation. Experience suggests that directional changes can only be interpreted if the data cover a reasonable number of time steps.
Kurzfassung
Sukzessionstheorien und dazugehörige Methoden für die Analyse von Zeitreihendaten werden vorgestellt und an Beispielen von publizierten Daten illustriert. Ausgehend von einer sehr weit gefassten Definition von Sukzession, die als gerichtete Veränderung der Vegetation verstanden wird, können Phänomene auf drei verschiedenen Perzeptionsstufen unterschieden werden: jener der Muster, der Prozesse und der Mechanismen. Das Muster ist das Erscheinungsbild, wie es in den Daten auftritt. Der Prozess ist eine Grammatik, die das Muster zu generieren erlaubt (Dale 1980). Beide können mit geeigneten statistischen Methoden untersucht werden. Mechanismen sind die Ursachen der Veränderungen. Manchmal kann von den Mustern und Prozessen auf Mechanismen geschlossen werden, doch können sie in der Regel nicht direkt beobachtet oder gemessen werden.
Die vorgestellten Methoden und Beispiele umfassen das statistische Erhebungskonzept von Langzeituntersuchungen, die Analyse von Gesellschaftsdynamik mittels Markovketten, Mustererkennung mittels Ordination, die Analyse zeitlicher Autokorrelation und eine Fuzzy-Ordination, um Zeitreihendaten einem Zeitvektor anzupassen. In den meisten Methoden können die Resultate auf die Existenz eines Zeittrends hin getestet werden, wobei Monte Carlo-Simulation zum Einsatz kommt. Die Erfahrungen zeigen, dass gerichtete Veränderungen in der Regel nur interpretiert werden können, wenn genügend Zeitschritte dokumentiert sind.
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Wildi, O. (2001). Statistical Design and Analysis in Long-Term Vegetation Monitoring. In: Burga, C.A., Kratochwil, A. (eds) Biomonitoring: General and Applied Aspects on Regional and Global Scales. Tasks for vegetation science, vol 35. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9686-2_2
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DOI: https://doi.org/10.1007/978-94-015-9686-2_2
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