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• W2017803478 abstract "Time series of population density are often used to seek deviations from logistic regulation by testing for a non-linear decline in per capita growth rate with density. Here I show that this method fails when the interval between observations is not matched by the timing of density impacts on growth. Time series overestimate instantaneous density impacts at low density and underestimate them at high density. More generally, logistic growth produces a deterministically decelerating decline in per capita growth with density if the interval between measures of population size exceeds any lag in density response. Deceleration arises independently out of stochastic density fluctuations, and under-compensating regulation. These multiple influences lead to the conclusion that sequential density estimates provide insufficient information on their own to reveal the identity of non-logistic growth processes. They can yield estimates of density compensation, however, which may suggest time lags in density dependence. Analysis of an empirical time series illustrates the issues." @default.
• W2017803478 created "2016-06-24" @default.
• W2017803478 creator A5027038365 @default.
• W2017803478 date "2008-06-01" @default.
• W2017803478 modified "2023-09-27" @default.
• W2017803478 title "Non-linear density dependence in time series is not evidence of non-logistic growth" @default.
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• W2017803478 doi "https://doi.org/10.1016/j.tpb.2008.02.003" @default.
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