# Tenhumberg

**Model complexity affects predicted transient population dynamics: A case study with**Brigitte Tenhumberg, University of Nebraska, Lincoln

*Acyrtosiphum pisum*Population matrix models are commonly used to predict population dynamics and the effects of short term management actions. Most researchers focus on long term dynamics when the stable stage distribution is reached. However, even in established populations, the assumption that the population growth follows the asymptotic growth is unwarranted in many cases. Disturbances such as environmental catastrophes, selective harvesting regimes and management actions significantly alter a population’s state distribution. The deviation from the stable state distribution changes the population dynamics, sometimes dramatically. The scale of the deviation and the time until the stable age distribution is achieved depend on the population matrix structure, parameter values, and the net reproductive value of a population. This begs the question: “How much model complexity is required to accurately characterize the transient behavior?” We conducted laboratory experiments with the aphid species Acyrtosiphum pisum (Harris) to parameterize stage structured models of different levels of complexity, and compared model predictions with independent, empirically measured population growth rates. The low complexity model (3x3 matrix) failed to predict the asymptotic damped oscillations observed in laboratory aphid populations, but the high complexity model (11 x 11 matrix) predictions were consistent with empirical data. Interestingly, a 3x3 matrix model of A. pisum by Gross et al. (1992) parameterized with field collected time series data does predict asymptotic oscillations in growth rates, but the oscillations have significantly larger amplitudes and shorter cycles than observed in our empirical data. All models predict the same long term population growth rate (λmax = 1.35). It is essential to consider the effects of model complexity on predicted dynamics; transient dynamics in particular are sensitive to both the structure and parameters of the model.

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