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TIME-SERIES ANALYSES APPLIED TO SEQUENCES OF NOTHOFAGUS GROWTH-RING MEASUREMENTS

R.C. WOOLLONS and D.A. NORTON
New Zealand Journal of Ecology
Vol. 13, No. 1 (1990), pp. 9-15
Stable URL: http://www.jstor.org/stable/24053263
Page Count: 7
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
TIME-SERIES ANALYSES APPLIED TO SEQUENCES OF NOTHOFAGUS GROWTH-RING MEASUREMENTS
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Abstract

Time-series analysis, a relatively uncommon technique in ecological studies, has been applied to annual tree growth-ring series. In agreement with earlier North American work, ARMA(1,1) models were found to be the predominant form for expressing stochastic growth processes, occurring in 58% of the 36 Nothofagus menziesii and N. solandri time-series examined. The remaining 42% conformed to an AR(1) process. The average parameter values of {0.79, 0.42} for the ARMA models are remarkably consistent with North American work. Such derived stochastic models should be regarded as average processes; analyses of first-order autocorrelation coefficients indicate fluctuations in absolute value within series, including some short periods of independence. An apparent preference for a specific ARMA model with species is better explained by the lengths of the series; a shorter time-series is likely to have a simpler stochastic model over time, by virtue of lesser precision associated with model parameters. Thus, 81% of the series longer than 200 years are modelled by an ARMA(1,1) process, while 78% of the series shorter than 200, are modelled by AR(1). It is suggested that although fitting Box-Jenkins stochastic models to various genera represents an interesting area of research, the approximate equivalence of the various models, and their part-dependence on series length, negates the need to locate an optimal process in all circumstances. The principal advantage of utilising Box-Jenkins models in this application is to render data more suitable for analysis with environmental variables, and to enhance cross-correlation and mean sensitivity.

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