Evaluating the Performance of Bayesian Removal Models for Estimating Population Density and Detecting Trends with Variable Detection Probability

Removal models have long been used to estimate population abundance by progressively capturing and removing individuals from a closed population. These models provide a valuable tool for ecological monitoring, but their accuracy depends heavily on assumptions about detection probability, which may decline over successive sampling passes. Traditional removal models assume constant detection probabilities, an assumption that is often violated in real-world applications. This thesis aims to advance hierarchical Bayesian models by accounting for variable detection probabilities, improving the reliability of abundance estimates and trend detection. By integrating simulation-based analyses with empirical data from Lahontan Cutthroat Trout (Oncorhynchus clarkia henshawi) populations, this study evaluates how different detection probability model-based structures influence model performance. Simulation results indicate that models accounting for variable detection probabilities produce less biased estimates, particularly in low-detection scenarios. However, when detection probabilities are high, the proposed model and traditional removal models produce indistinguishable estimates. Regardless of detection probability, both models successfully detect population trends over time. These findings underscore the importance of incorporating detection variability in removal models to enhance their utility for ecological monitoring.