Estimating population size and hidden demographic parameters with state-space modeling.

Area of Science:

• Ecology
• Population Dynamics
• Statistical Modeling

Background:

• Ecological time series data often contain process variability and measurement error.
• State-space modeling (SSM) is a technique used to disentangle these factors.
• Combining individual-based data with population counts improves ecological models.

Purpose of the Study:

• To extend maximum likelihood approaches in state-space modeling.
• To incorporate sex- and age-dependent counts into ecological models.
• To evaluate the performance of the enhanced SSM using Soay sheep data.

Main Methods:

• Developed an extended state-space modeling approach.
• Incorporated sex- and age-specific demographic data.
• Applied the model to Soay sheep population data from St. Kilda.
• Empirically evaluated model performance by data omission.

Main Results:

• Multivariate time series analysis enhanced the power of state-space modeling.
• Measurement error variance was smaller for males.
• Lamb sex ratio was skewed towards females and constant over time.
• Demographic parameter estimates aligned with mark-recapture-recovery analyses.
• Population size estimates were accurate even with missing count data.

Conclusions:

• The enhanced state-space modeling approach has significant potential for ecological research.
• The method is valuable for estimating hidden demographic parameters.
• It aids in planning population monitoring studies.
• It accurately estimates historical and future population sizes.