Modern fishing fleets have the capacity to over-exploit fish stocks. Inaccurate assessments could overestimate the stock size and as a result Total Allowable Catches (TAC) are set too high for sustainable stock conservation. Fisheries management need robust and reliable stock assessments to ensure that the species and environmental effect of fishing is sustainable. Since the demand for ecosystem based approaches to management has increased, the needs for improved estimates of un-assessed abundance have risen. Managers simply need to know how many fish left in the see and how much to limit the fishermen to fish to have sustainable fisheries. Therefore, accurate assessment of the market as well as by-catch stocks and records of true landings and discards are critical aspects of the scientific advice to the fisheries managers to accurately set TACs. Here, we consider the marine species that are left un-assessed. That is because they cannot be assessed by the existing methods. We therefore sought to fill the key gap with this matter. This thesis has five key elements. First we reviewed the stock assessment method with the emphasis on the length-structured models. Second, we produced a population model (so called survey-landings model) to make the use of survey frequency data extracted from International Bottom Trawl Survey and total annual landed biomass from commercial reports. Third, within a twin-experiment context and sensitivity analysis the model was assessed for accuracy and robustness in variability in initial parameter values and observational noise. Forth, applying the survey-landings model the population dynamics of the North Sea haddock was assessed and the results were compared with the International Council for Exploitation of the Sea assessment. Fifth, after the model proved to be reliable it is used as an alternative for age- or catch-at-length model, the population of the North Sea grey gurnards were modelled with confidence. This model enabled un-assessed species such as grey gurnards to be modelled and assessed for the first time.
|Qualification||Doctor of Philosophy|
|Award date||1 Oct 2017|
|Publication status||Published - 2017|