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Detecting the order of population dynamics from time series: nonlinearity causes spurious diagnosis
1Emeritus Professor, Departments of Entomology and Natural Resource Sciences, Washington State University, Pullman, Washington 99164, USA.
Abstract:
Partial autocorrelation and partial rate correlation functions are frequently used to detect the order of the endogenous process generating an observed population time series. Here we uncover a problem with this approach: the diagnosis of spurious second order autocorrelation due to strong nonlinearity in a first order endogenous process, as exemplified by time series data from a population of Soay sheep. Causes and a possible solution are discussed.
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