Estimating density dependence from time series of population age structure

Area of Science:

• Ecology
• Population Dynamics
• Mathematical Biology

Background:

• Population fluctuations are driven by various factors including stochasticity, life history time lags, and density dependence.
• Understanding density dependence is crucial for predicting population stability and managing wildlife populations.
• Existing models often simplify population structure, limiting their applicability to complex ecological scenarios.

Purpose of the Study:

• To develop a general method for modeling density dependence in populations with complex age or stage structures.
• To estimate the overall strength of density dependence by measuring the rate of return to equilibrium.
• To explore population forecasting using density-dependent reproductive value.

Main Methods:

• A general life history model incorporating density dependence within and among age/stage classes was developed.
• A novel method was created to estimate the strength of density dependence, quantified by the rate of return toward equilibrium.
• The method requires time series data of population age or stage structure, unlike simpler univariate models.

Main Results:

• The study successfully modeled density dependence in a general life history framework.
• The developed method provides a quantitative measure of density dependence strength.
• The approach was validated using a 21-year dataset of red deer (Cervus elaphus) age structure fluctuations.

Conclusions:

• The new method offers a more comprehensive analysis of population dynamics by incorporating age/stage structure.
• This approach enhances the ability to forecast population trends and understand ecological stability.
• The red deer case study demonstrates the practical application and effectiveness of the developed method.