Estimating equations for parameters in stochastic growth models from tag-recapture data.

Area of Science:

• Ecology
• Fisheries Science
• Statistical Modeling

Background:

• Accurate estimation of fish growth parameters is crucial for fisheries management.
• Traditional methods like least-squares may yield biased results, especially with complex models.
• The von Bertalanffy growth curve is widely used but can be limited in capturing biological complexities.

Purpose of the Study:

• To develop unbiased estimating functions for growth models incorporating stochasticity and covariates.
• To compare the performance of the proposed method against traditional least-squares approaches.
• To assess seasonal growth effects in rock lobsters using the new methodology.

Main Methods:

• Construction of unbiased estimating functions for a generalized class of growth models.
• Simulation studies employing seasonal growth models to evaluate method performance.
• Application of the developed model to real-world tag-recapture data from rock lobsters.

Main Results:

• The proposed method demonstrated superior performance in simulations, providing unbiased estimates.
• Least-squares methods showed substantial bias in estimating parameters for seasonal growth models.
• Analysis of rock lobster data revealed potential seasonal influences on growth patterns.

Conclusions:

• The developed unbiased estimating functions offer a robust alternative for analyzing complex growth models.
• This approach improves the reliability of parameter estimation in ecological and fisheries studies.
• The findings highlight the importance of considering seasonal effects and stochasticity in growth modeling.