Complex Population Dynamics in the Real World: Modeling the Influence of Time-Varying Parameters and Time Lags

We propose a class of complex population dynamic models that combines new time-varying parameters and second-order time lags for describing univariate ecological time series data. The Kalman filter and likelihood function were used to estimate parameters of all models in the class for 31 data sets,...

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Bibliographic Details
Published in:Ecology (Durham) 1998-09, Vol.79 (6), p.2193-2209
Main Authors: Zeng, Zheng, Nowierski, Robert M., Taper, Mark L., Dennis, Brian, Kemp, William P.
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Biological and medical sciences
complex population dynamics
Datasets
Demecology
density dependence
Ecological modeling
Ecology
Fundamental and applied biological sciences. Psychology
General aspects
Gompertz model
information criterion
Kalman filter
Kalman filtering
Kalman filters
maximum-likelihood function
Modeling
nonlinear density dependence
nonlinear time series
Parametric models
Population
Population biology
Population density
Population dynamics
Population ecology
population regulation
Ricker model
second order model
Time series
Time series models
time-varying parameters
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Summary:We propose a class of complex population dynamic models that combines new time-varying parameters and second-order time lags for describing univariate ecological time series data. The Kalman filter and likelihood function were used to estimate parameters of all models in the class for 31 data sets, and Schwarz's information criterion (SIC) was used to select the best model for each data set. Using the SIC method, models containing density-dependent processes were selected for 23 of the 31 cases examined, while models containing complex density-dependent processes were selected in 19 of these 23 density dependence cases. The density-dependent models identified by SIC had various linear or nonlinear forms, suggesting variable patterns of population regulation in nature. Population dynamics may combine density-dependent, inversely density-dependent, and density-independent processes, which may operate at different times and under different density ranges. These results suggest that our approach offers an advance for modeling complex population dynamics, discovering complex regulation processes, and estimating the distribution of extinction times in changing environments.
ISSN:0012-9658
1939-9170
DOI:10.1890/0012-9658(1998)079[2193:CPDITR]2.0.CO;2