| Summary: | Considering the uncertainties about model parameters and structure, observations, and the resulting predictions is crucial when managing natural resources. This thesis is compiled of four research articles that deal with uncertainties and change in marine fish populations. In all the articles, the Bayesian statistical modelling framework is adopted to account for various types of uncertainty. When modelling complex systems, one must recognize that all models are only approximations of reality and are based on the limited understanding about the system at that moment and on previously observed behaviour. Even if a model explains the recent behaviour and predicts how the system will respond in the near future, there is always a possibility that the system changes thus rendering the old model useless. Approaches to respond to sudden changes in the system’s dynamics are vital for successful resource management. Fish populations' renewal ability is mainly determined by their reproductive success. Thus, it is of utmost importance to be able to understand and model the reproductive dynamics of marine fish populations. In this thesis, fish reproduction is studied using models that link together the number of new individuals entering the adult population (recruits) and the size of the reproducing component of the population (spawning stock), namely, the stock-recruitment relationship. The focus in this work is on temporal and density-dependent variability in the stock-recruitment relationship. The temporal variability is studied using Bayesian change-point detection methods applied to detecting changes in the per capita reproductive output of four Atlantic cod populations by analysing recruit-per-spawner ratios, and in the parameters of the stock-recruitment relationship of four fish species in the southern Gulf of St. Lawrence. In this work, novel Bayesian methods are utilized to improve the handling of uncertainties about the parameters, the timings of the changes, and in short term predictions. This thesis presents ...
|
|---|