Quantitative population dynamics modeling is needed to evaluate the stock status and fisheries management plans to provide robust model and management strategies. Atlantic Weakfish (Cynoscion regalis), one important commercial and recreational fish species along the west coast of Atlantic Ocean that was found to be declining in recent years, was selected as an example species. My study aimed to explore the possible spatial heterogeneity of CPUE (catch per unit effort) year trend based on three fishery independent surveys and explore the influence of nonstationary natural mortality on the fisheries management through a MSE (Management Strategy Evaluation) algorithm based on the Weakfish stock assessment results. Five models for catch rate standardization were constructed based on the NEAMAP (NorthEast Area Monitoring and Assessment Program) survey data and the ‘best' two models were selected based on the ability to capture nonlinearity and spatial autocorrelation. The selected models were then used to fit the other two survey data to compare the CPUE year trend of Weakfish. Obvious differences in distribution pattern of Weakfish along latitude and longitude were detected from these three surveys as well as the CPUE year trend. To test the influence of the model selection on the MSE, five stock-recruitment models and two forms of statistical catch-at-age models were used to evaluate the fishery management strategies. The current biomass-based reference point tends to be high if the true population dynamics have nonstationary natural mortality. A flexible biomass based reference point to match the nonstationary process is recommended for future fisheries management. / Master of Science

Haul-by-haul steam trawler catch and effort data for 1918�23, 1937�43 and 1952�57, which covers a large portion of the history of steam trawling in the Australian South East Fishery, were examined in detail for the first time. There were 64,371 haul records in total. The catch-rate for all retained catch combined shows a strong decline overall, with a brief recovery during World War II, probably due to increased retention of previously discarded species. The fishing fleet moved to more distant fishing grounds and deeper waters as the catch-rate declined. The catch-rates of the main commercial species followed a similar pattern in a number of regions within the fishery. The catchrate of the primary target species � tiger flathead (Neoplatycephalus richardsoni) � dropped considerably from the early, very high, catch-rates. Chinaman leatherjacket (Nelusetta ayraudi) and latchet (Pterygotrigla polyommata) � species that were apparently abundant in the early years of the fishery � virtually disappeared from catches in later years. The appearance of greater catches of jackass morwong (Nemadactylus macropterus), redfish (Centroberyx affinis), and shark/skate during the war and afterwards was probably due to increased retention of catches of these species. The disappearance of certain species from the catch may be due to high fishing pressure alone, or to a combination of fishing pressure, changes in the shelf habitat possibly caused by the trawl gear, and environmental fluctuations. Catch-rates in weight per haul per species were standardised to annual indices of abundance using a log-linear model. Standardised annual index trends for flathead, latchet and leatherjacket indicate a strong to severe decline over the period covered by the data. All species showed seasonal patterns, but the peak season varied depending on the species. The distribution of standardised catch-rate by area also differed greatly by species, and no single area showed consistent differences across all species. Day trawls caught more flathead, redfish and latchet, while night trawls caught more morwong and leatherjacket. Moon phase had less influence on catch-rates than the other factors examined. Correlation of annual index trends with a number of annual mean environmental factors was examined and no strong correlations were found. Annual catches of the major commercial trawl species on the SE Australian shelf were estimated from recorded total trawl catches, catch species composition from subsamples and estimates of the rate of discarding. These annual catches, standardised indices of abundance and biological population parameters were used in single-species stock reduction models to estimate absolute biomass trends. Biological population parameters and the biomass estimates were used to calculate management reference point fishing mortality rates F0.1, Fspr30 and Fmsy. Results showed that simple plausible population models can be constructed that account for catches over the long period of time from 1915 to 1961. Simple mass-balance ecosystem models were built for the demersal community of the SE Australian shelf for 1915 and 1961 using the Ecopath software. Model inputs were consistent with a more comprehensive SE marine ecosystem model in development by CSIRO. The models demonstrate that biomass estimates produced by the single species stock reduction models can be consistently integrated into simple plausible massbalance ecosystem models. Modern stock assessments for the main commercial species in this fishery today mostly used data collected since about 1985. Abundance indices and total catch estimates from this study have been used in the most recent assessments for tiger flathead and morwong, allowing construction of the exploitation history for these species spanning almost 100 years. Use of the historical information has increased confidence in the estimates of the modern stock assessments � particularly management reference points, and has allowed us to quantify changes in fish abundance that have simply been documented anecdotally in the past.

Yellow perch (Perca flavescens) in Lake Erie support valuable commercial and recreational fisheries critical to the local economy and society. The study of yellow perch's temporal and spatial population dynamics is important for both stock assessment and fisheries management. I explore the spatial and temporal variation of the yellow perch population by analyzing the fishery-independent surveys in Lake Erie. Model-based approaches were developed to estimate the relative abundance index, which reflected the temporal variation of the population. I also used design-based approaches to deal with the situation in which population density varied both spatially and temporally. I first used model-based approaches to explore the spatial and temporal variation of the yellow perch population and to develop the relative abundance index needed. Generalized linear models (GLM), spatial generalized linear models (s-GLM), and generalized additive models (GAM) were compared by examining the goodness-of-fit, reduction of spatial autocorrelation, and prediction errors from cross-validation. The relationship between yellow perch density distribution and spatial and environmental factors was also studied. I found that GAM showed the best goodness-of-fit shown as AIC and lowest prediction errors but s-GLM resulted in the best reduction of spatial autocorrelation. Both performed better than GLM for yellow perch relative abundance index estimation. I then applied design-based approaches to study the spatial and temporal population dynamics of yellow perch through both practical data analysis and simulation. The currently used approach in Lake Erie is stratified random sampling (StRS). Traditional sampling designs (simple random sampling (SRS) and StRS) and adaptive sampling designs (adaptive two-phase sampling (ATS), adaptive cluster sampling (ACS), and adaptive two-stage sequential sampling (ATSS)) for fishery-independent surveys were compared. From accuracy and precision aspect, ATS performed better than the SRS, StRS, ACS and ATSS for yellow perch fishery-independent survey data in Lake Erie. Model-based approaches were further studied by including geostatistical models. The performance of the GLM and GAM models and geostatistical models (spatial interpolation) were compared when they are used to analyze the temporal and spatial variation of the yellow perch population through a simulation study. This is the first time that these two types of model- based approaches have been compared in fisheries. I found that arithmetic mean (AM) method was only preferred when neither environment factors nor spatial information of sampling locations were available. If the survey can not cover the distribution area of the population due to biased design or lack of sampling locations, GLMs and GAMs are preferable to spatial interpolation (SI). Otherwise, SI is a good alternative model to estimate relative abundance index. SI has rarely been realized in fisheries. Different models may be recommended for different species/fisheries when we estimate their spatial-temporal dynamics, and also the most appropriate survey designs may be different for different species. However, the criteria and approaches for the comparison of both model-based and design-based approaches will be applied for different species or fisheries. / Ph. D.

Dans cette thèse sur travaux, l’auteur part de deux travaux précédents sur la rentabilité de la fidélisation et la détermination du potentiel client pour faire un constat : une partie de la rentabilité de la fidélisation et du potentiel pour une marque est basée sur la part captable du chiffre d’affaire fait à la concurrence ; le taux de captation.Dans ce nouveau travail il s’agit de montrer que le taux de captation est basé sur l’élasticité du taux de nourriture. A partir d’une analyse de la littérature et de plusieurs modélisations sur des données de panel scannérisées, l’auteur démontre, sur plusieurs marchés, que l’élasticité du taux de nourriture est contrainte et prévisible.C’est donc en tenant compte de cet écart limité qu’une marque peut estimer le taux de captation et donc la rentabilité de la fidélisation ainsi que le potentiel client. / In this thesis based on works the author goes from two previous studies on the profitability of loyalty and customer potential determination to make a statement : part of the profitability of loyalty and of the potential for a brand is based on the reachable share of turnover done by the competition ; the catch rate.In this new work it is shown that the catch rate is based on the elasticity of the share of category requirement. From a review of the literature and several modeling on scanning panel data the author demonstrates on several markets that elasticity of the share of category requirement is limited and predictable.Then it’s in taking into account this small difference that a brand can estimate the “catchable” rate and therefore the profitability of loyalty and potential of a customer.