An individual-based probabilistic model for simulating fisheries population dynamics

Abstract The purpose of stock assessment is to support managers to provide intelligent decisions regarding removal from fish populations. Errors in assessment models may have devastating impacts on the population fitness and negative impacts on the economy of the resource users. Thus, accuracte estimations of population size, growth rates are critical for success. Evaluating and testing the behavior and performance of stock assessment models and assessing the consequences of model mis-specification and the impact of management strategies requires an operating model that accurately describe the dynamics of the target species, and can resolve spatial and seasonal changes. In addition, the most thorough evaluations of assessment models use an operating model that takes a different form than the assessment model. This paper presents an individual-based probabilistic model used to simulate the complex dynamics of populations and their associated fisheries. Various components of population dynamics are expressed as random Bernoulli trials in the model and detailed life and fishery histories of each individual are tracked over their life span. The simulation model is designed to be flexible so it can be used for different species and fisheries. It can simulate mixing among multiple stocks and link stock-recruit relationships to environmental factors. Furthermore, the model allows for flexibility in sub-models (e.g., growth and recruitment) and model assumptions (e.g., age- or size-dependent selectivity). This model enables the user to conduct various simulation studies, including testing the performance of assessment models under different assumptions, assessing the impacts of model mis-specification and evaluating management strategies.