Assessing the effect of density on population growth when modeling individual encounter data

Area of Science:

• Ecology
• Population Dynamics
• Conservation Biology

Background:

• Quantifying density dependence is crucial for population ecology and conservation.
• Distinguishing density-dependent from density-independent factors remains a challenge.

Purpose of the Study:

• To develop a hierarchical formulation of the temporal symmetry approach (Pradel model) for estimating density dependence strength.
• To integrate relative population size estimation and account for random variability in demographic rates.
• To provide a unified modeling framework for testing and quantifying density-dependent and independent factors.

Main Methods:

• Utilized a hierarchical formulation of the temporal symmetry approach (Pradel model).
• Incorporated a measure of relative population size to detect density dependence on population growth rate.
• Extended the model to include temporal random variability in demographic rates.

Main Results:

• The model successfully estimates the strength of density dependence from capture-mark-reencounter data.
• Density dependence was directly detected on population growth rate.
• Temporal variance in population growth rate, unexplained by density dependence, could be estimated.

Conclusions:

• The presented model-based approach effectively quantifies density-dependent and independent factors in population fluctuations.
• Including density dependence in modeling individual encounter data is valuable, even without auxiliary data.
• This framework enhances ecological theory and conservation applications.