Identifying Delayed Density Dependence in Time-Series Data

Abstract

I investigated the ability of statistical tests to detect delayed density dependence in series of abundances per generation. I generated time series containing delayed density dependence using two simple population models (a host-parasitoid model and a version of the Ricker equation) and analysed these using the tests for delayed density dependence of Turchin (1990), the lag 2 partial autocorrelation coefficient (PACF) and a novel modification of Pollard et al's (1987) test. All tests of delayed density dependence are of low statistical power, and so any delayed density dependence that is present may frequently be overlooked, particularly with short (< 25 generation) time series. The modification of Pollard et al's test was the best test for detecting delayed density dependence